experiment 2. checkpoint 1_010_000

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 274.74 +/- 20.58
+      value: 274.40 +/- 18.97
       name: mean_reward
       verified: false
 ---

args.yml

@@ -50,17 +50,17 @@
   - - sampler
     - tpe
   - - save_freq
-    - 40000
+    - 10000
   - - save_replay_buffer
     - false
   - - seed
-    - 4013127165
+    - 2587859772
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
-    - runs/LunarLander-v2__ppo__4013127165__1674537105
+    - runs/LunarLander-v2__ppo__2587859772__1674536198
   - - track
     - true
   - - trained_agent

ppo-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1d35571b720f2728889521bc0ee5d1b304ad4b1e68f1430a171ce400c3a365bf
-size 150358
+oid sha256:fb79fcdab12324ba22de2e5324739869a2bf16bf153014d7475c4596439486bb
+size 150362

ppo-LunarLander-v2/data

@@ -4,20 +4,20 @@
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         "__module__": "stable_baselines3.common.policies",
         "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
-        "__init__": "<function ActorCriticPolicy.__init__ at 0x7feefd666940>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7feefd6669d0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7feefd666a60>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7feefd666af0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7feefd666b80>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7feefd666c10>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7feefd666ca0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7feefd666d30>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7feefd666dc0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7feefd666e50>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7feefd666ee0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7feefd666f70>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fb416ce9940>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb416ce99d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb416ce9a60>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb416ce9af0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fb416ce9b80>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fb416ce9c10>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fb416ce9ca0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb416ce9d30>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fb416ce9dc0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb416ce9e50>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb416ce9ee0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb416ce9f70>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7feefd6626f0>"
+        "_abc_impl": "<_abc_data object at 0x7fb416ce66f0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -43,14 +43,14 @@
         "_np_random": "RandomState(MT19937)"
     },
     "n_envs": 1,
-    "num_timesteps": 1015808,
+    "num_timesteps": 1010000,
     "_total_timesteps": 1000000,
     "_num_timesteps_at_start": 0,
     "seed": 0,
     "action_noise": null,
-    "start_time": 1674537112774442748,
+    "start_time": 1674536206442318020,
     "learning_rate": 0.0003,
-    "tensorboard_log": "runs/LunarLander-v2__ppo__4013127165__1674537105/LunarLander-v2",
+    "tensorboard_log": "runs/LunarLander-v2__ppo__2587859772__1674536198/LunarLander-v2",
     "lr_schedule": {
         ":type:": "<class 'function'>",
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@@ -64,16 +64,16 @@
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.015808000000000044,
+    "_current_progress_remaining": 0.0005760000000000209,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": 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