Next commit

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 241.37 +/- 13.60
+      value: 272.25 +/- 12.91
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f240c57eb80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f240c57ec10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f240c57eca0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f240c57ed30>", "_build": "<function ActorCriticPolicy._build at 0x7f240c57edc0>", "forward": "<function ActorCriticPolicy.forward at 0x7f240c57ee50>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f240c57eee0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f240c57ef70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f240c583040>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f240c5830d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f240c583160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f240c57a720>"}, "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:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 64, "num_timesteps": 10092544, "_total_timesteps": 10000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652031351.1824777, "learning_rate": 0.0003, "tensorboard_log": "lunar_lander", "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:": "gAWVswAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS0CFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.009254400000000107, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 770, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 2048, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.13.0-40-generic-x86_64-with-glibc2.17 #45~20.04.1-Ubuntu SMP Mon Apr 4 09:38:31 UTC 2022", "Python": "3.8.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0", "GPU Enabled": "True", "Numpy": "1.21.2", "Gym": "0.21.0"}}
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f81c0bc5a70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f81c0bc5b00>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f81c0bc5b90>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f81c0bc5c20>", "_build": "<function ActorCriticPolicy._build at 0x7f81c0bc5cb0>", "forward": "<function ActorCriticPolicy.forward at 0x7f81c0bc5d40>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f81c0bc5dd0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f81c0bc5e60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f81c0bc5ef0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f81c0bc5f80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f81c0bca050>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f81c0c1d120>"}, "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:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 32, "num_timesteps": 10027008, "_total_timesteps": 10000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652030234.9069953, "learning_rate": 0.0003, "tensorboard_log": "lunar_lander", "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:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0027007999999999477, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 1530, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 1024, "n_epochs": 10, "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.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:80232b567df4a96b9a76281f0533a51023178e1b6f004234bdeb0f44dde5e36e
-size 146139
+oid sha256:01289f1bb091e31527655e9a11871df8559f6ef9de620a68ba247d08ead100c2
+size 144737

ppo-lunarlander/data

@@ -4,19 +4,19 @@
         ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
         "__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 0x7f240c57eb80>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f240c57ec10>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f240c57eca0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f240c57ed30>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f240c57edc0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f240c57ee50>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f240c57eee0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f240c57ef70>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f240c583040>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f240c5830d0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f240c583160>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f81c0bc5a70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f81c0bc5b00>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f81c0bc5b90>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f81c0bc5c20>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f81c0bc5cb0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f81c0bc5d40>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f81c0bc5dd0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f81c0bc5e60>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f81c0bc5ef0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f81c0bc5f80>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f81c0bca050>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f240c57a720>"
+        "_abc_impl": "<_abc_data object at 0x7f81c0c1d120>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -41,52 +41,52 @@
         "dtype": "int64",
         "_np_random": null
     },
-    "n_envs": 64,
-    "num_timesteps": 10092544,
+    "n_envs": 32,
+    "num_timesteps": 10027008,
     "_total_timesteps": 10000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1652031351.1824777,
+    "start_time": 1652030234.9069953,
     "learning_rate": 0.0003,
     "tensorboard_log": "lunar_lander",
     "lr_schedule": {
         ":type:": "<class 'function'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "gAWVswAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS0CFlIwBQ5R0lFKULg=="
+        ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="
     },
     "_last_original_obs": null,
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.009254400000000107,
+    "_current_progress_remaining": -0.0027007999999999477,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 770,
+    "_n_updates": 1530,
     "n_steps": 2048,
     "gamma": 0.99,
     "gae_lambda": 0.95,
     "ent_coef": 0.0,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
-    "batch_size": 2048,
+    "batch_size": 1024,
     "n_epochs": 10,
     "clip_range": {
         ":type:": "<class 'function'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "clip_range_vf": null,
     "normalize_advantage": true,

ppo-lunarlander/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:114f126a8a3400a544c003f278fcb915f91ce9f7f5af955153123c91386937cd
-size 84829
+oid sha256:944491495574afb86934d073625f88c8a1d255f69e5764ed81e45554d71d1a7b
+size 84893

ppo-lunarlander/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:85036d3d16913d4bb58a3d42a61b687964bcd511ad76278498378dfb1da4e180
+oid sha256:e1fa4bcb98856d3df1ccba2401a55c10e8f294ea90d0dd9e669101399297fcf0
 size 43201

ppo-lunarlander/system_info.txt

@@ -1,7 +1,7 @@
-OS: Linux-5.13.0-40-generic-x86_64-with-glibc2.17 #45~20.04.1-Ubuntu SMP Mon Apr 4 09:38:31 UTC 2022
-Python: 3.8.13
+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
+PyTorch: 1.11.0+cu113
 GPU Enabled: True
-Numpy: 1.21.2
+Numpy: 1.21.6
 Gym: 0.21.0

replay.mp4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0de9f05bf5db607731f24d383871c504b356fd127d1fcfcf3a3f3fcfe6fe237d
-size 211768
+oid sha256:50dfed89979232705eebaf1a9f2b0d2d969245be415978f00caaef3c0c7c491f
+size 191448

results.json

@@ -1 +1 @@
-{"mean_reward": 241.3715440725653, "std_reward": 13.597524576283577, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-08T19:05:05.193580"}
+{"mean_reward": 272.2463888198258, "std_reward": 12.90972997543762, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-08T19:49:25.981963"}