Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 251.98 +/- 18.99
+      value: 266.83 +/- 25.30
       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 0x7f7531ae55a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7531ae5630>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7531ae56c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7531ae5750>", "_build": "<function ActorCriticPolicy._build at 0x7f7531ae57e0>", "forward": "<function ActorCriticPolicy.forward at 0x7f7531ae5870>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7531ae5900>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7531ae5990>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7531ae5a20>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7531ae5ab0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7531ae5b40>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7531ae5bd0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f7531add7c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1685446139075111900, "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": 248, "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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "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 0x7fbe62aa4f70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbe62aa5000>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbe62aa5090>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbe62aa5120>", "_build": "<function ActorCriticPolicy._build at 0x7fbe62aa51b0>", "forward": "<function ActorCriticPolicy.forward at 0x7fbe62aa5240>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbe62aa52d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbe62aa5360>", "_predict": "<function ActorCriticPolicy._predict at 0x7fbe62aa53f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbe62aa5480>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbe62aa5510>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbe62aa55a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fbe62c96d40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1685658593118053024, "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": 320, "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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "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-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:36fd6135a312c20f5a688bb520ecd3ed871ac1c99b9eee5d095fd9d616b3f457
-size 146759
+oid sha256:02fbe77cb95831fcef5887f7b075dea3a283cb7a385dc93b6d964389177f5cbe
+size 146731

ppo-LunarLander-v2/data

@@ -4,20 +4,20 @@
         ":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 0x7f7531ae55a0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7531ae5630>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7531ae56c0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7531ae5750>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f7531ae57e0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f7531ae5870>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7531ae5900>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7531ae5990>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f7531ae5a20>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7531ae5ab0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7531ae5b40>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7531ae5bd0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fbe62aa4f70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbe62aa5000>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbe62aa5090>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbe62aa5120>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fbe62aa51b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fbe62aa5240>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbe62aa52d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbe62aa5360>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fbe62aa53f0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbe62aa5480>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbe62aa5510>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbe62aa55a0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f7531add7c0>"
+        "_abc_impl": "<_abc._abc_data object at 0x7fbe62c96d40>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -26,12 +26,12 @@
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1685446139075111900,
+    "start_time": 1685658593118053024,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -45,13 +45,13 @@
     "_stats_window_size": 100,
     "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": 248,
+    "_n_updates": 320,
     "observation_space": {
         ":type:": "<class 'gymnasium.spaces.box.Box'>",
         ":serialized:": "gAWVcAIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoB4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoECiWCAAAAAAAAAABAQEBAQEBAZRoFEsIhZRoGHSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBAoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaApLCIWUaBh0lFKUjARoaWdolGgQKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgKSwiFlGgYdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=",

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:fbfb1e51bdce7adaf214b83760607994a59334391de4ff1a5abe13c48fe0d896
+oid sha256:e2d84c1467d7dedb1cbadafefd5cdd36b7657292a7416f47743dd4748c5af63d
 size 87929

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9992e3f7678ff362208f44cfe5f950fa33478380b9a96e5823f89c68610a382d
+oid sha256:a52d7ff05c184fdef930867a0a25673b20df691a5c33dc9de628ffb1214829ef
 size 43329

results.json

@@ -1 +1 @@
-{"mean_reward": 251.97575089761273, "std_reward": 18.988330527186715, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-30T13:52:37.944242"}
+{"mean_reward": 266.8283527923601, "std_reward": 25.298715325685166, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-06-01T23:08:20.860245"}