Trained for 1000000 steps with 32 env

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: -142.88 +/- 44.36
+      value: 274.67 +/- 15.11
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f1d0b73c290>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1d0b73c320>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1d0b73c3b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1d0b73c440>", "_build": "<function ActorCriticPolicy._build at 0x7f1d0b73c4d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f1d0b73c560>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1d0b73c5f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1d0b73c680>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1d0b73c710>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1d0b73c7a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1d0b73c830>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f1d0b783a50>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 114688, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1653661515.295161, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gASVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.1468799999999999, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 28, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f1d0b73c290>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1d0b73c320>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1d0b73c3b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1d0b73c440>", "_build": "<function ActorCriticPolicy._build at 0x7f1d0b73c4d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f1d0b73c560>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1d0b73c5f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1d0b73c680>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1d0b73c710>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1d0b73c7a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1d0b73c830>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f1d0b783a50>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 32, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1653662103.7252762, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVqAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSyCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlHSUYi4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 155, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 5, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2-second-try.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:51d3d1182e3a4656aa1ab9d38271e815f04e274974909c9d299e36c7c8bd7c01
+size 144898

ppo-LunarLander-v2-second-try/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.0

ppo-LunarLander-v2-second-try/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f1d0b73c290>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1d0b73c320>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1d0b73c3b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1d0b73c440>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f1d0b73c4d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f1d0b73c560>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1d0b73c5f0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f1d0b73c680>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1d0b73c710>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1d0b73c7a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1d0b73c830>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f1d0b783a50>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 32,
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1653662103.7252762,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gASVqAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSyCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlHSUYi4="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.015808000000000044,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 155,
+    "n_steps": 1024,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 5,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo-LunarLander-v2-second-try/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:690e121bc6e5a64db82027f68fb8a23997c0ca2a7e7b9bbf716c41c46fdb8e46
+size 84893

ppo-LunarLander-v2-second-try/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:237a2e65bc4ad4a6fef0bea669e1ed667e174097d46dced7eb4c5ad7e8110c52
+size 43201

ppo-LunarLander-v2-second-try/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-LunarLander-v2-second-try/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

replay.mp4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:345e1b0f5e28f992ad0fee6f27a7d6e9b5ade6b1ef5aff3fb2eeb2c0150ab6e0
-size 217820
+oid sha256:5eceecd4b5f66efe1d341c50d6ca90a390c0c8c0a739d1776c52f82e74e6bd75
+size 227421

results.json

@@ -1 +1 @@
-{"mean_reward": -142.87579962805322, "std_reward": 44.36413770051194, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-27T14:29:34.688378"}
+{"mean_reward": 274.67309156237405, "std_reward": 15.105709954422517, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-27T14:54:12.890118"}