More train steps

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 201.54 +/- 84.43
+      value: 279.94 +/- 16.05
       name: mean_reward
       verified: false
 ---

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 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 0x7fa2f632b2e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa2f632b370>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa2f632b400>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa2f632b490>", "_build": "<function ActorCriticPolicy._build at 0x7fa2f632b520>", "forward": "<function ActorCriticPolicy.forward at 0x7fa2f632b5b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa2f632b640>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa2f632b6d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa2f632b760>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa2f632b7f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa2f632b880>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa2f632b910>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fa2f6322ec0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1011200, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1688433664068122457, "learning_rate": 0.0005, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.011200000000000099, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 790, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 800, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.05, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
+{"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 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 0x7c5cb629a320>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c5cb629a3b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c5cb629a440>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c5cb629a4d0>", "_build": "<function ActorCriticPolicy._build at 0x7c5cb629a560>", "forward": "<function ActorCriticPolicy.forward at 0x7c5cb629a5f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c5cb629a680>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c5cb629a710>", "_predict": "<function ActorCriticPolicy._predict at 0x7c5cb629a7a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c5cb629a830>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c5cb629a8c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c5cb629a950>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7c5cb628fd40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 2031616, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690398554460776350, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 620, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo_lunar_lander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc2cd780bc51a7a4c234191dfb9b5c58b072d17bef4ff6a089620fb3d279db0d
+size 146624

ppo_lunar_lander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo_lunar_lander-v2/data

@@ -0,0 +1,99 @@
+{
+    "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 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 0x7c5cb629a320>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c5cb629a3b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c5cb629a440>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c5cb629a4d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7c5cb629a560>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7c5cb629a5f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c5cb629a680>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c5cb629a710>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7c5cb629a7a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c5cb629a830>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c5cb629a8c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c5cb629a950>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7c5cb628fd40>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 2031616,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1690398554460776350,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.015808000000000044,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 620,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "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": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

ppo_lunar_lander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:987a22454a2d1563e27a6bf906096c1709eaac786c81182c9422bd9e35386dc2
+size 87929

ppo_lunar_lander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f967e18446738e4a94058af1827c6d19e0981bcf06eb0c1b59cc074dd7bc90a7
+size 43329

ppo_lunar_lander-v2/pytorch_variables.pth

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

ppo_lunar_lander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.6
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -1 +1 @@
-{"mean_reward": 201.53706459999998, "std_reward": 84.42538189280488, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-04T01:56:14.054035"}
+{"mean_reward": 279.940483, "std_reward": 16.05392367540404, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-26T19:55:21.056798"}