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+---
+tags:
+- reinforcement-learning
+- atari-alien
+- atari-amidar
+- atari-assault
+- atari-asterix
+- atari-asteroids
+- atari-atlantis
+- atari-bankheist
+- atari-battlezone
+- atari-beamrider
+- atari-berzerk
+- atari-bowling
+- atari-boxing
+- atari-breakout
+- atari-centipede
+- atari-choppercommand
+- atari-crazyclimber
+- atari-defender
+- atari-demonattack
+- atari-doubledunk
+- atari-enduro
+- atari-fishingderby
+- atari-freeway
+- atari-frostbite
+- atari-gopher
+- atari-gravitar
+- atari-hero
+- atari-icehockey
+- atari-jamesbond
+- atari-kangaroo
+- atari-krull
+- atari-kungfumaster
+- atari-montezumarevenge
+- atari-mspacman
+- atari-namethisgame
+- atari-phoenix
+- atari-pitfall
+- atari-pong
+- atari-privateeye
+- atari-qbert
+- atari-riverraid
+- atari-roadrunner
+- atari-robotank
+- atari-seaquest
+- atari-skiing
+- atari-solaris
+- atari-spaceinvaders
+- atari-stargunner
+- atari-surround
+- atari-tennis
+- atari-timepilot
+- atari-tutankham
+- atari-upndown
+- atari-venture
+- atari-videopinball
+- atari-wizardofwor
+- atari-yarsrevenge
+- atari-zaxxon
+- babyai-action-obj-door
+- babyai-blocked-unlock-pickup
+- babyai-boss-level-no-unlock
+- babyai-boss-level
+- babyai-find-obj-s5
+- babyai-go-to-door
+- babyai-go-to-imp-unlock
+- babyai-go-to-local
+- babyai-go-to-obj-door
+- babyai-go-to-obj
+- babyai-go-to-red-ball-grey
+- babyai-go-to-red-ball-no-dists
+- babyai-go-to-red-ball
+- babyai-go-to-red-blue-ball
+- babyai-go-to-seq
+- babyai-go-to
+- babyai-key-corridor
+- babyai-mini-boss-level
+- babyai-move-two-across-s8n9
+- babyai-one-room-s8
+- babyai-open-door
+- babyai-open-doors-order-n4
+- babyai-open-red-door
+- babyai-open-two-doors
+- babyai-open
+- babyai-pickup-above
+- babyai-pickup-dist
+- babyai-pickup-loc
+- babyai-pickup
+- babyai-put-next-local
+- babyai-put-next
+- babyai-synth-loc
+- babyai-synth-seq
+- babyai-synth
+- babyai-unblock-pickup
+- babyai-unlock-local
+- babyai-unlock-pickup
+- babyai-unlock-to-unlock
+- babyai-unlock
+- metaworld-assembly
+- metaworld-basketball
+- metaworld-bin-picking
+- metaworld-box-close
+- metaworld-button-press-topdown-wall
+- metaworld-button-press-topdown
+- metaworld-button-press-wall
+- metaworld-button-press
+- metaworld-coffee-button
+- metaworld-coffee-pull
+- metaworld-coffee-push
+- metaworld-dial-turn
+- metaworld-disassemble
+- metaworld-door-close
+- metaworld-door-lock
+- metaworld-door-open
+- metaworld-door-unlock
+- metaworld-drawer-close
+- metaworld-drawer-open
+- metaworld-faucet-close
+- metaworld-faucet-open
+- metaworld-hammer
+- metaworld-hand-insert
+- metaworld-handle-press-side
+- metaworld-handle-press
+- metaworld-handle-pull-side
+- metaworld-handle-pull
+- metaworld-lever-pull
+- metaworld-peg-insert-side
+- metaworld-peg-unplug-side
+- metaworld-pick-out-of-hole
+- metaworld-pick-place-wall
+- metaworld-pick-place
+- metaworld-plate-slide-back-side
+- metaworld-plate-slide-back
+- metaworld-plate-slide-side
+- metaworld-plate-slide
+- metaworld-push-back
+- metaworld-push-wall
+- metaworld-push
+- metaworld-reach-wall
+- metaworld-reach
+- metaworld-shelf-place
+- metaworld-soccer
+- metaworld-stick-pull
+- metaworld-stick-push
+- metaworld-sweep-into
+- metaworld-sweep
+- metaworld-window-close
+- metaworld-window-open
+- mujoco-ant
+- mujoco-doublependulum
+- mujoco-halfcheetah
+- mujoco-hopper
+- mujoco-humanoid
+- mujoco-pendulum
+- mujoco-pusher
+- mujoco-reacher
+- mujoco-standup
+- mujoco-swimmer
+- mujoco-walker
+datasets: jat-project/jat-dataset
+pipeline_tag: reinforcement-learning
+model-index:
+- name: jat-project/jat
+  results:
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Atari 57
+      type: atari
+    metrics:
+    - type: iqm_expert_normalized_total_reward
+      value: 0.06 [0.06, 0.06]
+      name: IQM expert normalized total reward
+    - type: iqm_human_normalized_total_reward
+      value: 0.17 [0.16, 0.17]
+      name: IQM human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: BabyAI
+      type: babyai
+    metrics:
+    - type: iqm_expert_normalized_total_reward
+      value: 0.99 [0.99, 0.99]
+      name: IQM expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: MetaWorld
+      type: metaworld
+    metrics:
+    - type: iqm_expert_normalized_total_reward
+      value: 0.68 [0.67, 0.69]
+      name: IQM expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: MuJoCo
+      type: mujoco
+    metrics:
+    - type: iqm_expert_normalized_total_reward
+      value: 0.81 [0.80, 0.82]
+      name: IQM expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Alien
+      type: atari-alien
+    metrics:
+    - type: total_reward
+      value: 1085.90 +/- 396.36
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.05 +/- 0.02
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.12 +/- 0.06
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Amidar
+      type: atari-amidar
+    metrics:
+    - type: total_reward
+      value: 41.26 +/- 28.57
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.02 +/- 0.01
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.02 +/- 0.02
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Assault
+      type: atari-assault
+    metrics:
+    - type: total_reward
+      value: 772.89 +/- 59.34
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.04 +/- 0.00
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 1.06 +/- 0.11
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Asterix
+      type: atari-asterix
+    metrics:
+    - type: total_reward
+      value: 778.50 +/- 428.97
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.16 +/- 0.12
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.07 +/- 0.05
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Asteroids
+      type: atari-asteroids
+    metrics:
+    - type: total_reward
+      value: 1423.60 +/- 538.79
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.00 +/- 0.00
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.02 +/- 0.01
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Atlantis
+      type: atari-atlantis
+    metrics:
+    - type: total_reward
+      value: 23541.00 +/- 10376.72
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.03 +/- 0.03
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.66 +/- 0.64
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Bank Heist
+      type: atari-bankheist
+    metrics:
+    - type: total_reward
+      value: 685.50 +/- 157.92
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.51 +/- 0.12
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.91 +/- 0.21
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Battle Zone
+      type: atari-battlezone
+    metrics:
+    - type: total_reward
+      value: 12950.00 +/- 4306.68
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.04 +/- 0.01
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.34 +/- 0.12
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Beam Rider
+      type: atari-beamrider
+    metrics:
+    - type: total_reward
+      value: 762.04 +/- 243.25
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.01 +/- 0.01
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.02 +/- 0.01
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Berzerk
+      type: atari-berzerk
+    metrics:
+    - type: total_reward
+      value: 523.90 +/- 161.95
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.01 +/- 0.00
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.16 +/- 0.06
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Bowling
+      type: atari-bowling
+    metrics:
+    - type: total_reward
+      value: 29.99 +/- 11.49
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.00 +/- 0.00
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.05 +/- 0.08
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Boxing
+      type: atari-boxing
+    metrics:
+    - type: total_reward
+      value: 87.00 +/- 22.57
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.89 +/- 0.23
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 7.24 +/- 1.88
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Breakout
+      type: atari-breakout
+    metrics:
+    - type: total_reward
+      value: 9.16 +/- 5.76
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.01 +/- 0.01
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.26 +/- 0.20
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Centipede
+      type: atari-centipede
+    metrics:
+    - type: total_reward
+      value: 4461.72 +/- 2188.80
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.25 +/- 0.23
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.24 +/- 0.22
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Chopper Command
+      type: atari-choppercommand
+    metrics:
+    - type: total_reward
+      value: 1497.00 +/- 723.11
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.01 +/- 0.01
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.10 +/- 0.11
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Crazy Climber
+      type: atari-crazyclimber
+    metrics:
+    - type: total_reward
+      value: 52850.00 +/- 31617.86
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.25 +/- 0.19
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 1.68 +/- 1.26
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Defender
+      type: atari-defender
+    metrics:
+    - type: total_reward
+      value: 10627.50 +/- 4473.21
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.02 +/- 0.01
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.49 +/- 0.28
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Demon Attack
+      type: atari-demonattack
+    metrics:
+    - type: total_reward
+      value: 315.10 +/- 279.01
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.00 +/- 0.00
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.09 +/- 0.15
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Double Dunk
+      type: atari-doubledunk
+    metrics:
+    - type: total_reward
+      value: 0.08 +/- 11.61
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.47 +/- 0.29
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.53 +/- 0.33
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Enduro
+      type: atari-enduro
+    metrics:
+    - type: total_reward
+      value: 111.49 +/- 27.36
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.05 +/- 0.01
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.13 +/- 0.03
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Fishing Derby
+      type: atari-fishingderby
+    metrics:
+    - type: total_reward
+      value: -55.21 +/- 19.35
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.37 +/- 0.20
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.28 +/- 0.15
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Freeway
+      type: atari-freeway
+    metrics:
+    - type: total_reward
+      value: 24.12 +/- 1.64
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.71 +/- 0.05
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.81 +/- 0.06
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Frostbite
+      type: atari-frostbite
+    metrics:
+    - type: total_reward
+      value: 617.30 +/- 686.11
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.04 +/- 0.05
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.13 +/- 0.16
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Gopher
+      type: atari-gopher
+    metrics:
+    - type: total_reward
+      value: 2947.20 +/- 1448.32
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.03 +/- 0.02
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 1.25 +/- 0.67
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Gravitar
+      type: atari-gravitar
+    metrics:
+    - type: total_reward
+      value: 1030.50 +/- 719.20
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.22 +/- 0.19
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.27 +/- 0.23
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: H.E.R.O.
+      type: atari-hero
+    metrics:
+    - type: total_reward
+      value: 6997.95 +/- 2562.51
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.14 +/- 0.06
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.20 +/- 0.09
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Ice Hockey
+      type: atari-icehockey
+    metrics:
+    - type: total_reward
+      value: -3.77 +/- 3.10
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.20 +/- 0.09
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.61 +/- 0.26
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: James Bond
+      type: atari-jamesbond
+    metrics:
+    - type: total_reward
+      value: 187.50 +/- 72.24
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.01 +/- 0.00
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.58 +/- 0.26
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Kangaroo
+      type: atari-kangaroo
+    metrics:
+    - type: total_reward
+      value: 124.00 +/- 156.92
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.14 +/- 0.30
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.02 +/- 0.05
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Krull
+      type: atari-krull
+    metrics:
+    - type: total_reward
+      value: 8933.00 +/- 1358.65
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.75 +/- 0.14
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 6.87 +/- 1.27
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Kung-Fu Master
+      type: atari-kungfumaster
+    metrics:
+    - type: total_reward
+      value: 100.00 +/- 142.13
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: -0.00 +/- 0.00
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: -0.01 +/- 0.01
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Montezuma's Revenge
+      type: atari-montezumarevenge
+    metrics:
+    - type: total_reward
+      value: 0.00 +/- 0.00
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.00 +/- 0.00
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.00 +/- 0.00
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Ms. Pacman
+      type: atari-mspacman
+    metrics:
+    - type: total_reward
+      value: 1516.30 +/- 376.72
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.18 +/- 0.06
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.18 +/- 0.06
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Name This Game
+      type: atari-namethisgame
+    metrics:
+    - type: total_reward
+      value: 3798.60 +/- 1361.64
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.07 +/- 0.07
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.26 +/- 0.24
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Phoenix
+      type: atari-phoenix
+    metrics:
+    - type: total_reward
+      value: 1267.50 +/- 1013.72
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.00 +/- 0.00
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.08 +/- 0.16
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: PitFall
+      type: atari-pitfall
+    metrics:
+    - type: total_reward
+      value: -287.36 +/- 492.82
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: -0.25 +/- 2.16
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: -0.01 +/- 0.07
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Pong
+      type: atari-pong
+    metrics:
+    - type: total_reward
+      value: -11.03 +/- 11.29
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.23 +/- 0.27
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.27 +/- 0.32
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Private Eye
+      type: atari-privateeye
+    metrics:
+    - type: total_reward
+      value: 96.00 +/- 19.60
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.95 +/- 0.26
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.00 +/- 0.00
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Q*Bert
+      type: atari-qbert
+    metrics:
+    - type: total_reward
+      value: 1701.75 +/- 1912.56
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.04 +/- 0.04
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.12 +/- 0.14
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: River Raid
+      type: atari-riverraid
+    metrics:
+    - type: total_reward
+      value: 2793.10 +/- 693.84
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.11 +/- 0.05
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.09 +/- 0.04
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Road Runner
+      type: atari-roadrunner
+    metrics:
+    - type: total_reward
+      value: 7699.00 +/- 3446.61
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.10 +/- 0.04
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.98 +/- 0.44
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Robotank
+      type: atari-robotank
+    metrics:
+    - type: total_reward
+      value: 16.36 +/- 5.24
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.18 +/- 0.07
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 1.46 +/- 0.54
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Seaquest
+      type: atari-seaquest
+    metrics:
+    - type: total_reward
+      value: 515.20 +/- 141.51
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.18 +/- 0.06
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.01 +/- 0.00
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Skiing
+      type: atari-skiing
+    metrics:
+    - type: total_reward
+      value: -29396.08 +/- 3289.80
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: -1.93 +/- 0.52
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: -0.96 +/- 0.26
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Solaris
+      type: atari-solaris
+    metrics:
+    - type: total_reward
+      value: 988.20 +/- 487.42
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: -2.11 +/- 4.15
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: -0.02 +/- 0.04
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Space Invaders
+      type: atari-spaceinvaders
+    metrics:
+    - type: total_reward
+      value: 339.50 +/- 164.05
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.01 +/- 0.01
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.13 +/- 0.11
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Star Gunner
+      type: atari-stargunner
+    metrics:
+    - type: total_reward
+      value: 978.00 +/- 638.37
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.00 +/- 0.00
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.03 +/- 0.07
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Surround
+      type: atari-surround
+    metrics:
+    - type: total_reward
+      value: -8.22 +/- 1.19
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.09 +/- 0.06
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.11 +/- 0.07
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Tennis
+      type: atari-tennis
+    metrics:
+    - type: total_reward
+      value: -22.38 +/- 2.22
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.04 +/- 0.06
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.04 +/- 0.07
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Time Pilot
+      type: atari-timepilot
+    metrics:
+    - type: total_reward
+      value: 9534.00 +/- 2577.76
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.09 +/- 0.04
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 3.59 +/- 1.55
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Tutankham
+      type: atari-tutankham
+    metrics:
+    - type: total_reward
+      value: 40.20 +/- 14.51
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.10 +/- 0.05
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.18 +/- 0.09
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Up and Down
+      type: atari-upndown
+    metrics:
+    - type: total_reward
+      value: 6072.00 +/- 2283.30
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.01 +/- 0.01
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.50 +/- 0.20
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Venture
+      type: atari-venture
+    metrics:
+    - type: total_reward
+      value: 0.00 +/- 0.00
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.00 +/- 0.00
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.00 +/- 0.00
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Video Pinball
+      type: atari-videopinball
+    metrics:
+    - type: total_reward
+      value: 7943.01 +/- 8351.21
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.02 +/- 0.02
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.45 +/- 0.47
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Wizard of Wor
+      type: atari-wizardofwor
+    metrics:
+    - type: total_reward
+      value: 1306.00 +/- 1139.81
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.02 +/- 0.02
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.18 +/- 0.27
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Yars Revenge
+      type: atari-yarsrevenge
+    metrics:
+    - type: total_reward
+      value: 8597.41 +/- 4291.81
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.02 +/- 0.02
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.11 +/- 0.08
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Zaxxon
+      type: atari-zaxxon
+    metrics:
+    - type: total_reward
+      value: 896.00 +/- 1172.68
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.01 +/- 0.02
+      name: Expert normalized total reward
+    - type: human_normalized_total_reward
+      value: 0.09 +/- 0.13
+      name: Human normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Action Obj Door
+      type: babyai-action-obj-door
+    metrics:
+    - type: total_reward
+      value: 0.95 +/- 0.13
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.94 +/- 0.22
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Blocked Unlock Pickup
+      type: babyai-blocked-unlock-pickup
+    metrics:
+    - type: total_reward
+      value: 0.95 +/- 0.01
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.00 +/- 0.01
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Boss Level No Unlock
+      type: babyai-boss-level-no-unlock
+    metrics:
+    - type: total_reward
+      value: 0.44 +/- 0.45
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.43 +/- 0.51
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Boss Level
+      type: babyai-boss-level
+    metrics:
+    - type: total_reward
+      value: 0.48 +/- 0.45
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.48 +/- 0.51
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Find Obj S5
+      type: babyai-find-obj-s5
+    metrics:
+    - type: total_reward
+      value: 0.95 +/- 0.03
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.00 +/- 0.04
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Go To Door
+      type: babyai-go-to-door
+    metrics:
+    - type: total_reward
+      value: 0.99 +/- 0.01
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.00 +/- 0.01
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Go To Imp Unlock
+      type: babyai-go-to-imp-unlock
+    metrics:
+    - type: total_reward
+      value: 0.50 +/- 0.44
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.56 +/- 0.59
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Go To Local
+      type: babyai-go-to-local
+    metrics:
+    - type: total_reward
+      value: 0.88 +/- 0.14
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.94 +/- 0.18
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Go To Obj Door
+      type: babyai-go-to-obj-door
+    metrics:
+    - type: total_reward
+      value: 0.98 +/- 0.04
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.97 +/- 0.08
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Go To Obj
+      type: babyai-go-to-obj
+    metrics:
+    - type: total_reward
+      value: 0.93 +/- 0.04
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.99 +/- 0.05
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Go To Red Ball Grey
+      type: babyai-go-to-red-ball-grey
+    metrics:
+    - type: total_reward
+      value: 0.91 +/- 0.06
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.99 +/- 0.08
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Go To Red Ball No Dists
+      type: babyai-go-to-red-ball-no-dists
+    metrics:
+    - type: total_reward
+      value: 0.93 +/- 0.03
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.00 +/- 0.04
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Go To Red Ball
+      type: babyai-go-to-red-ball
+    metrics:
+    - type: total_reward
+      value: 0.91 +/- 0.08
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.98 +/- 0.11
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Go To Red Blue Ball
+      type: babyai-go-to-red-blue-ball
+    metrics:
+    - type: total_reward
+      value: 0.88 +/- 0.11
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.96 +/- 0.13
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Go To Seq
+      type: babyai-go-to-seq
+    metrics:
+    - type: total_reward
+      value: 0.73 +/- 0.34
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.75 +/- 0.40
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Go To
+      type: babyai-go-to
+    metrics:
+    - type: total_reward
+      value: 0.80 +/- 0.27
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.85 +/- 0.35
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Key Corridor
+      type: babyai-key-corridor
+    metrics:
+    - type: total_reward
+      value: 0.88 +/- 0.10
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.97 +/- 0.11
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Mini Boss Level
+      type: babyai-mini-boss-level
+    metrics:
+    - type: total_reward
+      value: 0.69 +/- 0.35
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.76 +/- 0.43
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Move Two Across S8N9
+      type: babyai-move-two-across-s8n9
+    metrics:
+    - type: total_reward
+      value: 0.03 +/- 0.15
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.03 +/- 0.16
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: One Room S8
+      type: babyai-one-room-s8
+    metrics:
+    - type: total_reward
+      value: 0.92 +/- 0.03
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.00 +/- 0.04
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Open Door
+      type: babyai-open-door
+    metrics:
+    - type: total_reward
+      value: 0.99 +/- 0.00
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.00 +/- 0.01
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Open Doors Order N4
+      type: babyai-open-doors-order-n4
+    metrics:
+    - type: total_reward
+      value: 0.96 +/- 0.11
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.97 +/- 0.13
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Open Red Door
+      type: babyai-open-red-door
+    metrics:
+    - type: total_reward
+      value: 0.92 +/- 0.02
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.00 +/- 0.03
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Open Two Doors
+      type: babyai-open-two-doors
+    metrics:
+    - type: total_reward
+      value: 0.98 +/- 0.00
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.00 +/- 0.00
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Open
+      type: babyai-open
+    metrics:
+    - type: total_reward
+      value: 0.93 +/- 0.11
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.97 +/- 0.13
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Pickup Above
+      type: babyai-pickup-above
+    metrics:
+    - type: total_reward
+      value: 0.92 +/- 0.06
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.01 +/- 0.07
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Pickup Dist
+      type: babyai-pickup-dist
+    metrics:
+    - type: total_reward
+      value: 0.88 +/- 0.13
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.03 +/- 0.18
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Pickup Loc
+      type: babyai-pickup-loc
+    metrics:
+    - type: total_reward
+      value: 0.84 +/- 0.20
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.91 +/- 0.24
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Pickup
+      type: babyai-pickup
+    metrics:
+    - type: total_reward
+      value: 0.72 +/- 0.34
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.77 +/- 0.40
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Put Next Local
+      type: babyai-put-next-local
+    metrics:
+    - type: total_reward
+      value: 0.60 +/- 0.36
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.65 +/- 0.39
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Put Next S7N4
+      type: babyai-put-next
+    metrics:
+    - type: total_reward
+      value: 0.82 +/- 0.26
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.86 +/- 0.27
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Synth Loc
+      type: babyai-synth-loc
+    metrics:
+    - type: total_reward
+      value: 0.82 +/- 0.31
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.85 +/- 0.38
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Synth Seq
+      type: babyai-synth-seq
+    metrics:
+    - type: total_reward
+      value: 0.57 +/- 0.44
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.57 +/- 0.50
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Synth
+      type: babyai-synth
+    metrics:
+    - type: total_reward
+      value: 0.68 +/- 0.39
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.69 +/- 0.47
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Unblock Pickup
+      type: babyai-unblock-pickup
+    metrics:
+    - type: total_reward
+      value: 0.76 +/- 0.33
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.82 +/- 0.39
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Unlock Local
+      type: babyai-unlock-local
+    metrics:
+    - type: total_reward
+      value: 0.98 +/- 0.01
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.00 +/- 0.01
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Unlock Pickup
+      type: babyai-unlock-pickup
+    metrics:
+    - type: total_reward
+      value: 0.76 +/- 0.03
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.01 +/- 0.04
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Unlock To Unlock
+      type: babyai-unlock-to-unlock
+    metrics:
+    - type: total_reward
+      value: 0.86 +/- 0.29
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.89 +/- 0.30
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Unlock
+      type: babyai-unlock
+    metrics:
+    - type: total_reward
+      value: 0.55 +/- 0.42
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.63 +/- 0.50
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Assembly
+      type: metaworld-assembly
+    metrics:
+    - type: total_reward
+      value: 238.32 +/- 32.98
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.96 +/- 0.16
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Basketball
+      type: metaworld-basketball
+    metrics:
+    - type: total_reward
+      value: 1.59 +/- 0.43
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: -0.00 +/- 0.00
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: BinPicking
+      type: metaworld-bin-picking
+    metrics:
+    - type: total_reward
+      value: 374.18 +/- 168.23
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.88 +/- 0.40
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Box Close
+      type: metaworld-box-close
+    metrics:
+    - type: total_reward
+      value: 510.10 +/- 117.47
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.99 +/- 0.27
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Button Press Topdown Wall
+      type: metaworld-button-press-topdown-wall
+    metrics:
+    - type: total_reward
+      value: 260.07 +/- 67.75
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.49 +/- 0.14
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Button Press Topdown
+      type: metaworld-button-press-topdown
+    metrics:
+    - type: total_reward
+      value: 265.16 +/- 77.93
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.51 +/- 0.17
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Button Press Wall
+      type: metaworld-button-press-wall
+    metrics:
+    - type: total_reward
+      value: 621.75 +/- 137.13
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.92 +/- 0.21
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Button Press
+      type: metaworld-button-press
+    metrics:
+    - type: total_reward
+      value: 556.75 +/- 198.85
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.86 +/- 0.33
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Coffee Button
+      type: metaworld-coffee-button
+    metrics:
+    - type: total_reward
+      value: 250.50 +/- 266.92
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.31 +/- 0.38
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Coffee Pull
+      type: metaworld-coffee-pull
+    metrics:
+    - type: total_reward
+      value: 55.13 +/- 96.96
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.20 +/- 0.38
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Coffee Push
+      type: metaworld-coffee-push
+    metrics:
+    - type: total_reward
+      value: 269.17 +/- 237.82
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.54 +/- 0.48
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Dial Turn
+      type: metaworld-dial-turn
+    metrics:
+    - type: total_reward
+      value: 738.22 +/- 168.43
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.93 +/- 0.22
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Disassemble
+      type: metaworld-disassemble
+    metrics:
+    - type: total_reward
+      value: 39.14 +/- 11.85
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: -0.47 +/- 4.70
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Door Close
+      type: metaworld-door-close
+    metrics:
+    - type: total_reward
+      value: 528.17 +/- 29.90
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.00 +/- 0.06
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Door Lock
+      type: metaworld-door-lock
+    metrics:
+    - type: total_reward
+      value: 676.51 +/- 192.68
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.81 +/- 0.28
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Door Open
+      type: metaworld-door-open
+    metrics:
+    - type: total_reward
+      value: 572.76 +/- 57.53
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.98 +/- 0.11
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Door Unlock
+      type: metaworld-door-unlock
+    metrics:
+    - type: total_reward
+      value: 654.94 +/- 260.64
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.79 +/- 0.37
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Drawer Close
+      type: metaworld-drawer-close
+    metrics:
+    - type: total_reward
+      value: 663.02 +/- 214.51
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.73 +/- 0.29
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Drawer Open
+      type: metaworld-drawer-open
+    metrics:
+    - type: total_reward
+      value: 489.07 +/- 21.28
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.99 +/- 0.06
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Faucet Close
+      type: metaworld-faucet-close
+    metrics:
+    - type: total_reward
+      value: 361.32 +/- 72.28
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.22 +/- 0.14
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Faucet Open
+      type: metaworld-faucet-open
+    metrics:
+    - type: total_reward
+      value: 637.86 +/- 134.50
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.85 +/- 0.29
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Hammer
+      type: metaworld-hammer
+    metrics:
+    - type: total_reward
+      value: 691.72 +/- 25.25
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.00 +/- 0.04
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Hand Insert
+      type: metaworld-hand-insert
+    metrics:
+    - type: total_reward
+      value: 719.57 +/- 99.26
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.97 +/- 0.13
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Handle Press Side
+      type: metaworld-handle-press-side
+    metrics:
+    - type: total_reward
+      value: 84.25 +/- 113.34
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.03 +/- 0.14
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Handle Press
+      type: metaworld-handle-press
+    metrics:
+    - type: total_reward
+      value: 731.94 +/- 261.90
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.84 +/- 0.34
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Handle Pull Side
+      type: metaworld-handle-pull-side
+    metrics:
+    - type: total_reward
+      value: 233.11 +/- 199.49
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.60 +/- 0.52
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Handle Pull
+      type: metaworld-handle-pull
+    metrics:
+    - type: total_reward
+      value: 501.29 +/- 209.45
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.74 +/- 0.32
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Lever Pull
+      type: metaworld-lever-pull
+    metrics:
+    - type: total_reward
+      value: 250.18 +/- 228.59
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.34 +/- 0.41
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Peg Insert Side
+      type: metaworld-peg-insert-side
+    metrics:
+    - type: total_reward
+      value: 288.02 +/- 157.87
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.91 +/- 0.50
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Peg Unplug Side
+      type: metaworld-peg-unplug-side
+    metrics:
+    - type: total_reward
+      value: 68.48 +/- 125.34
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.14 +/- 0.28
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Pick Out Of Hole
+      type: metaworld-pick-out-of-hole
+    metrics:
+    - type: total_reward
+      value: 2.08 +/- 0.05
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.00 +/- 0.00
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Pick Place Wall
+      type: metaworld-pick-place-wall
+    metrics:
+    - type: total_reward
+      value: 6.87 +/- 44.99
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.02 +/- 0.10
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Pick Place
+      type: metaworld-pick-place
+    metrics:
+    - type: total_reward
+      value: 264.18 +/- 195.69
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.63 +/- 0.47
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Plate Slide Back Side
+      type: metaworld-plate-slide-back-side
+    metrics:
+    - type: total_reward
+      value: 697.54 +/- 137.79
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.95 +/- 0.20
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Plate Slide Back
+      type: metaworld-plate-slide-back
+    metrics:
+    - type: total_reward
+      value: 196.80 +/- 1.73
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.24 +/- 0.00
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Plate Slide Side
+      type: metaworld-plate-slide-side
+    metrics:
+    - type: total_reward
+      value: 122.61 +/- 24.52
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.16 +/- 0.04
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Plate Slide
+      type: metaworld-plate-slide
+    metrics:
+    - type: total_reward
+      value: 497.42 +/- 168.74
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.93 +/- 0.37
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Push Back
+      type: metaworld-push-back
+    metrics:
+    - type: total_reward
+      value: 91.41 +/- 115.05
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.08 +/- 1.37
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Push Wall
+      type: metaworld-push-wall
+    metrics:
+    - type: total_reward
+      value: 116.49 +/- 208.05
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.15 +/- 0.28
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Push
+      type: metaworld-push
+    metrics:
+    - type: total_reward
+      value: 604.25 +/- 261.90
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.80 +/- 0.35
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Reach Wall
+      type: metaworld-reach-wall
+    metrics:
+    - type: total_reward
+      value: 634.57 +/- 231.40
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.81 +/- 0.38
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Reach
+      type: metaworld-reach
+    metrics:
+    - type: total_reward
+      value: 325.27 +/- 159.21
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.33 +/- 0.30
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Shelf Place
+      type: metaworld-shelf-place
+    metrics:
+    - type: total_reward
+      value: 124.60 +/- 112.83
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.52 +/- 0.47
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Soccer
+      type: metaworld-soccer
+    metrics:
+    - type: total_reward
+      value: 364.50 +/- 175.45
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.97 +/- 0.47
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Stick Pull
+      type: metaworld-stick-pull
+    metrics:
+    - type: total_reward
+      value: 398.64 +/- 205.60
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.76 +/- 0.39
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Stick Push
+      type: metaworld-stick-push
+    metrics:
+    - type: total_reward
+      value: 158.29 +/- 264.59
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.25 +/- 0.42
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Sweep Into
+      type: metaworld-sweep-into
+    metrics:
+    - type: total_reward
+      value: 775.30 +/- 119.00
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.97 +/- 0.15
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Sweep
+      type: metaworld-sweep
+    metrics:
+    - type: total_reward
+      value: 15.64 +/- 9.29
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.01 +/- 0.02
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Window Close
+      type: metaworld-window-close
+    metrics:
+    - type: total_reward
+      value: 423.33 +/- 203.92
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.69 +/- 0.38
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Window Open
+      type: metaworld-window-open
+    metrics:
+    - type: total_reward
+      value: 593.10 +/- 54.83
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.00 +/- 0.10
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Ant
+      type: mujoco-ant
+    metrics:
+    - type: total_reward
+      value: 5268.02 +/- 1495.39
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.90 +/- 0.25
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Inverted Double Pendulum
+      type: mujoco-doublependulum
+    metrics:
+    - type: total_reward
+      value: 4750.14 +/- 931.20
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.51 +/- 0.10
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Half Cheetah
+      type: mujoco-halfcheetah
+    metrics:
+    - type: total_reward
+      value: 6659.69 +/- 409.71
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.90 +/- 0.05
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Hopper
+      type: mujoco-hopper
+    metrics:
+    - type: total_reward
+      value: 1835.93 +/- 532.21
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.99 +/- 0.29
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Humanoid
+      type: mujoco-humanoid
+    metrics:
+    - type: total_reward
+      value: 697.44 +/- 108.06
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.09 +/- 0.02
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Inverted Pendulum
+      type: mujoco-pendulum
+    metrics:
+    - type: total_reward
+      value: 116.34 +/- 20.19
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.23 +/- 0.04
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Pusher
+      type: mujoco-pusher
+    metrics:
+    - type: total_reward
+      value: -26.33 +/- 6.32
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.99 +/- 0.05
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Reacher
+      type: mujoco-reacher
+    metrics:
+    - type: total_reward
+      value: -6.06 +/- 2.64
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.99 +/- 0.07
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Humanoid Standup
+      type: mujoco-standup
+    metrics:
+    - type: total_reward
+      value: 118125.15 +/- 24880.28
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 0.35 +/- 0.10
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Swimmer
+      type: mujoco-swimmer
+    metrics:
+    - type: total_reward
+      value: 93.26 +/- 3.78
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.01 +/- 0.04
+      name: Expert normalized total reward
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: Walker 2d
+      type: mujoco-walker
+    metrics:
+    - type: total_reward
+      value: 4662.43 +/- 762.67
+      name: Total reward
+    - type: expert_normalized_total_reward
+      value: 1.01 +/- 0.16
+      name: Expert normalized total reward
+---
+
+# Model Card for Jat
+
+This is a multi-modal and multi-task model.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** The JAT Team
+- **License:** Apache 2.0
+
+### Model Sources
+
+- **Repository:** <https://github.com/huggingface/jat>
+- **Paper:** Coming soon
+- **Demo:** Coming soon
+
+## Training
+
+The model was trained on the following tasks:
+
+- Alien
+- Amidar
+- Assault
+- Asterix
+- Asteroids
+- Atlantis
+- Bank Heist
+- Battle Zone
+- Beam Rider
+- Berzerk
+- Bowling
+- Boxing
+- Breakout
+- Centipede
+- Chopper Command
+- Crazy Climber
+- Defender
+- Demon Attack
+- Double Dunk
+- Enduro
+- Fishing Derby
+- Freeway
+- Frostbite
+- Gopher
+- Gravitar
+- H.E.R.O.
+- Ice Hockey
+- James Bond
+- Kangaroo
+- Krull
+- Kung-Fu Master
+- Montezuma's Revenge
+- Ms. Pacman
+- Name This Game
+- Phoenix
+- PitFall
+- Pong
+- Private Eye
+- Q*Bert
+- River Raid
+- Road Runner
+- Robotank
+- Seaquest
+- Skiing
+- Solaris
+- Space Invaders
+- Star Gunner
+- Surround
+- Tennis
+- Time Pilot
+- Tutankham
+- Up and Down
+- Venture
+- Video Pinball
+- Wizard of Wor
+- Yars Revenge
+- Zaxxon
+- Action Obj Door
+- Blocked Unlock Pickup
+- Boss Level No Unlock
+- Boss Level
+- Find Obj S5
+- Go To Door
+- Go To Imp Unlock
+- Go To Local
+- Go To Obj Door
+- Go To Obj
+- Go To Red Ball Grey
+- Go To Red Ball No Dists
+- Go To Red Ball
+- Go To Red Blue Ball
+- Go To Seq
+- Go To
+- Key Corridor
+- Mini Boss Level
+- Move Two Across S8N9
+- One Room S8
+- Open Door
+- Open Doors Order N4
+- Open Red Door
+- Open Two Doors
+- Open
+- Pickup Above
+- Pickup Dist
+- Pickup Loc
+- Pickup
+- Put Next Local
+- Put Next S7N4
+- Synth Loc
+- Synth Seq
+- Synth
+- Unblock Pickup
+- Unlock Local
+- Unlock Pickup
+- Unlock To Unlock
+- Unlock
+- Assembly
+- Basketball
+- BinPicking
+- Box Close
+- Button Press Topdown Wall
+- Button Press Topdown
+- Button Press Wall
+- Button Press
+- Coffee Button
+- Coffee Pull
+- Coffee Push
+- Dial Turn
+- Disassemble
+- Door Close
+- Door Lock
+- Door Open
+- Door Unlock
+- Drawer Close
+- Drawer Open
+- Faucet Close
+- Faucet Open
+- Hammer
+- Hand Insert
+- Handle Press Side
+- Handle Press
+- Handle Pull Side
+- Handle Pull
+- Lever Pull
+- Peg Insert Side
+- Peg Unplug Side
+- Pick Out Of Hole
+- Pick Place Wall
+- Pick Place
+- Plate Slide Back Side
+- Plate Slide Back
+- Plate Slide Side
+- Plate Slide
+- Push Back
+- Push Wall
+- Push
+- Reach Wall
+- Reach
+- Shelf Place
+- Soccer
+- Stick Pull
+- Stick Push
+- Sweep Into
+- Sweep
+- Window Close
+- Window Open
+- Ant
+- Inverted Double Pendulum
+- Half Cheetah
+- Hopper
+- Humanoid
+- Inverted Pendulum
+- Pusher
+- Reacher
+- Humanoid Standup
+- Swimmer
+- Walker 2d
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+from transformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("jat-project/jat")
+```