Improved version of model (longer training)

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: -419.09 +/- 131.06
+      value: 245.59 +/- 24.70
       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 0x7f8f00149940>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8f001499d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8f00149a60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8f00149af0>", "_build": "<function ActorCriticPolicy._build at 0x7f8f00149b80>", "forward": "<function ActorCriticPolicy.forward at 0x7f8f00149c10>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f8f00149ca0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8f00149d30>", "_predict": "<function ActorCriticPolicy._predict at 0x7f8f00149dc0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8f00149e50>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8f00149ee0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8f00149f70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f8f001462a0>"}, "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": 16384, "_total_timesteps": 5000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1676304345237137072, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAEAAAAAAAABAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -2.2768, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 8, "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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+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 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 0x7f6fafd54670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6fafd54700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6fafd54790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6fafd54820>", "_build": "<function ActorCriticPolicy._build at 0x7f6fafd548b0>", "forward": "<function ActorCriticPolicy.forward at 0x7f6fafd54940>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6fafd549d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6fafd54a60>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6fafd54af0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6fafd54b80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6fafd54c10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6fafd54ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6fafd4fae0>"}, "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": 1676326980501009587, "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+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "False", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6107b7c95d29b892b479e95ff5c0aa52c071ad9a69797c0faebf4ff5555edf9f
+size 146913

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-LunarLander-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7f6fafd54670>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6fafd54700>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6fafd54790>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6fafd54820>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f6fafd548b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f6fafd54940>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6fafd549d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6fafd54a60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f6fafd54af0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6fafd54b80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6fafd54c10>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6fafd54ca0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f6fafd4fae0>"
+    },
+    "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": 1676326980501009587,
+    "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:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d7455954792cf1c0e9040f1baf7fd2343e93507cc6de810a24dd0359d66615d3
+size 87545

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e7d0572501c65b134343ca885fdad6923b1cc641398366828e916daa4de4c452
+size 43265

ppo-LunarLander-v2/pytorch_variables.pth

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

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: False
+- Numpy: 1.21.6
+- Gym: 0.21.0

results.json

@@ -1 +1 @@
-{"mean_reward": -419.0855409538373, "std_reward": 131.0637299826633, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-13T16:12:36.930315"}
+{"mean_reward": 245.5897021919074, "std_reward": 24.699460569492032, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-13T22:53:39.087974"}