PPO LunarLander-v2 3,000,000 training steps agent model card

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 252.05 +/- 14.15
+      value: 272.45 +/- 35.84
       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 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 0x7f5d32f22d30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5d32f22dc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5d32f22e50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5d32f22ee0>", "_build": "<function ActorCriticPolicy._build at 0x7f5d32f22f70>", "forward": "<function ActorCriticPolicy.forward at 0x7f5d32f27040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5d32f270d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5d32f27160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5d32f271f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5d32f27280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5d32f27310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5d32f26120>"}, "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": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1672499953712496536, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_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:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "False", "Numpy": "1.21.6", "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 0x7f5d32f22d30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5d32f22dc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5d32f22e50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5d32f22ee0>", "_build": "<function ActorCriticPolicy._build at 0x7f5d32f22f70>", "forward": "<function ActorCriticPolicy.forward at 0x7f5d32f27040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5d32f270d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5d32f27160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5d32f271f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5d32f27280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5d32f27310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5d32f26120>"}, "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": 16, "num_timesteps": 3014656, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1672501975372589562, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 736, "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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "False", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2-3e6-ppo-steps.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:11d5a0a236386db2643657bd5cf8c283ae7841343219642e26e8f4c5e58c83e2
+size 146587

ppo-LunarLander-v2-3e6-ppo-steps/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

ppo-LunarLander-v2-3e6-ppo-steps/data

@@ -0,0 +1,94 @@
+{
+    "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 0x7f5d32f22d30>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5d32f22dc0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5d32f22e50>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5d32f22ee0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f5d32f22f70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f5d32f27040>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5d32f270d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f5d32f27160>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5d32f271f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5d32f27280>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5d32f27310>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f5d32f26120>"
+    },
+    "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": 16,
+    "num_timesteps": 3014656,
+    "_total_timesteps": 3000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1672501975372589562,
+    "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.004885333333333408,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 736,
+    "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
+}

ppo-LunarLander-v2-3e6-ppo-steps/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df318a24cdc25899978412785ee2977c53df650130e74335c8e922842d809713
+size 87545

ppo-LunarLander-v2-3e6-ppo-steps/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f750390d4c368434c7a43dcc594ae05316c6b538105da6b25d2b756f980b3b67
+size 43073

ppo-LunarLander-v2-3e6-ppo-steps/pytorch_variables.pth

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

ppo-LunarLander-v2-3e6-ppo-steps/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.16
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: False
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -1 +1 @@
-{"mean_reward": 252.04687822003285, "std_reward": 14.151408097152533, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-31T15:39:29.162616"}
+{"mean_reward": 272.4462303134669, "std_reward": 35.83715995643372, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-31T16:35:53.956909"}