Trained 5M for Steps PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 257.13 +/- 16.43
+      value: 288.32 +/- 17.79
       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 0x7ac65e5a83a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ac65e5a8430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ac65e5a84c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ac65e5a8550>", "_build": "<function ActorCriticPolicy._build at 0x7ac65e5a85e0>", "forward": "<function ActorCriticPolicy.forward at 0x7ac65e5a8670>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ac65e5a8700>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ac65e5a8790>", "_predict": "<function ActorCriticPolicy._predict at 0x7ac65e5a8820>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ac65e5a88b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ac65e5a8940>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ac65e5a89d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ac65e595200>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1697375438016375020, "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": 268, "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": 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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "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 0x7ac65e5a83a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ac65e5a8430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ac65e5a84c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ac65e5a8550>", "_build": "<function ActorCriticPolicy._build at 0x7ac65e5a85e0>", "forward": "<function ActorCriticPolicy.forward at 0x7ac65e5a8670>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ac65e5a8700>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ac65e5a8790>", "_predict": "<function ActorCriticPolicy._predict at 0x7ac65e5a8820>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ac65e5a88b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ac65e5a8940>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ac65e5a89d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ac65e595200>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 2015232, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1697379739392754526, "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": -0.007616000000000067, "_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": 1256, "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": 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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2-5M.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95e388d55d5d3a1d2e9672791099bb073d8c6d27d713416c15f79433a3b7642f
+size 146628

ppo-LunarLander-v2-5M/_stable_baselines3_version

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

ppo-LunarLander-v2-5M/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 0x7ac65e5a83a0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ac65e5a8430>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ac65e5a84c0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ac65e5a8550>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ac65e5a85e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ac65e5a8670>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ac65e5a8700>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ac65e5a8790>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ac65e5a8820>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ac65e5a88b0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ac65e5a8940>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ac65e5a89d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ac65e595200>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 2015232,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1697379739392754526,
+    "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": -0.007616000000000067,
+    "_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": 1256,
+    "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": 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,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

ppo-LunarLander-v2-5M/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d0674b587fa170399e642d3820ab65c52d9c4f5c276fbee9721589f942e38bf
+size 87929

ppo-LunarLander-v2-5M/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:505ed54693ef279be558b6e57ccdda13f414b4fc4b86c73e8558e9a4b9c2083f
+size 43329

ppo-LunarLander-v2-5M/pytorch_variables.pth

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

ppo-LunarLander-v2-5M/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -1 +1 @@
-{"mean_reward": 257.1313592876333, "std_reward": 16.429514339450996, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-15T13:41:05.857370"}
+{"mean_reward": 288.32379378910275, "std_reward": 17.786236243734482, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-15T14:45:42.296682"}