Initial commit

Browse files

Files changed (9) hide show

README.md +1 -1
a2c-AntBulletEnv-v0.zip +1 -1
a2c-AntBulletEnv-v0/data +17 -17
a2c-AntBulletEnv-v0/policy.optimizer.pth +1 -1
a2c-AntBulletEnv-v0/policy.pth +1 -1
config.json +1 -1
replay.mp4 +2 -2
results.json +1 -1
vec_normalize.pkl +1 -1

README.md CHANGED Viewed

@@ -16,7 +16,7 @@ model-index:
       type: AntBulletEnv-v0
     metrics:
     - type: mean_reward
-      value: 1461.19 +/- 59.11
       name: mean_reward
       verified: false
 ---

       type: AntBulletEnv-v0
     metrics:
     - type: mean_reward
+      value: 1599.83 +/- 71.52
       name: mean_reward
       verified: false
 ---

a2c-AntBulletEnv-v0.zip CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3e6a1dece826b8ce37c7dd231f0fff5aa8fcaefe6fca43fa308c7c3ef9e2e543
 size 129260

 version https://git-lfs.github.com/spec/v1
+oid sha256:8f0bb0a220af1b58c40c9e67f501ac9871c517506e9a3836a1939c6ec15f7b44
 size 129260

a2c-AntBulletEnv-v0/data CHANGED Viewed

@@ -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 0x7fb6622aa280>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb6622aa310>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb6622aa3a0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb6622aa430>",
-        "_build": "<function ActorCriticPolicy._build at 0x7fb6622aa4c0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7fb6622aa550>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fb6622aa5e0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb6622aa670>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7fb6622aa700>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb6622aa790>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb6622aa820>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb6622aa8b0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fb6622a5690>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -64,7 +64,7 @@
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1674387816803976993,
     "learning_rate": 0.00096,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -73,7 +73,7 @@
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -81,7 +81,7 @@
     },
     "_last_original_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
     },
     "_episode_num": 0,
     "use_sde": true,
@@ -89,7 +89,7 @@
     "_current_progress_remaining": 0.0,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",

         ":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 0x7fcc13e491f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcc13e49280>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcc13e49310>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcc13e493a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fcc13e49430>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fcc13e494c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fcc13e49550>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcc13e495e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fcc13e49670>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcc13e49700>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcc13e49790>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcc13e49820>",
         "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fcc13e43780>"
     },
     "verbose": 1,
     "policy_kwargs": {
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
+    "start_time": 1674466811053211816,
     "learning_rate": 0.00096,
     "tensorboard_log": null,
     "lr_schedule": {
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
     },
     "_last_original_obs": {
         ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
     },
     "_episode_num": 0,
     "use_sde": true,
     "_current_progress_remaining": 0.0,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",

a2c-AntBulletEnv-v0/policy.optimizer.pth CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5e8cd5ce1c85908a5adfa39519ce973961b008af0bf4b57b53a3e1aee38c87af
 size 56190

 version https://git-lfs.github.com/spec/v1
+oid sha256:31bd62b288ab4f5589d4a61bc7b512f1ec7999934c237344c20af19ae6fd3f26
 size 56190

a2c-AntBulletEnv-v0/policy.pth CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0ec57c506015542605d48917c2bf2b8febb9b66fde083e87ac9493c4f422b50a
 size 56958

 version https://git-lfs.github.com/spec/v1
+oid sha256:828a7c6f5e8e7e33f8321c999692db30ac16fd44a341c655be7746b4d4184154
 size 56958

config.json CHANGED Viewed

@@ -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 0x7fb6622aa280>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb6622aa310>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb6622aa3a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb6622aa430>", "_build": "<function ActorCriticPolicy._build at 0x7fb6622aa4c0>", "forward": "<function ActorCriticPolicy.forward at 0x7fb6622aa550>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fb6622aa5e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb6622aa670>", "_predict": "<function ActorCriticPolicy._predict at 0x7fb6622aa700>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb6622aa790>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb6622aa820>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb6622aa8b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fb6622a5690>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVZwIAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLHIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWcAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/lGgKSxyFlIwBQ5R0lFKUjARoaWdolGgSKJZwAAAAAAAAAAAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH+UaApLHIWUaBV0lFKUjA1ib3VuZGVkX2JlbG93lGgSKJYcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLHIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUaCFLHIWUaBV0lFKUjApfbnBfcmFuZG9tlE51Yi4=", "dtype": "float32", "_shape": [28], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674387816803976993, "learning_rate": 0.00096, "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:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "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": "True", "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 0x7fcc13e491f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcc13e49280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcc13e49310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcc13e493a0>", "_build": "<function ActorCriticPolicy._build at 0x7fcc13e49430>", "forward": "<function ActorCriticPolicy.forward at 0x7fcc13e494c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fcc13e49550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcc13e495e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fcc13e49670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcc13e49700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcc13e49790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcc13e49820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fcc13e43780>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [28], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674466811053211816, "learning_rate": 0.00096, "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:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "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": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

replay.mp4 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ad59c9ed048db9e8c152a1abc8961904891bd0521f85a1f5b027aa2450421945
-size 1076318

 version https://git-lfs.github.com/spec/v1
+oid sha256:ee51c8acb2cd21633ad1c2a2257d56403f1df092a1f2ed8cb585a7cacfb98150
+size 1072231

results.json CHANGED Viewed

	@@ -1 +1 @@
1	- {"mean_reward": ~~1461~~.~~1899053041009~~, "std_reward": 59.~~109504475764034~~, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-~~22T13~~:15:45.~~055657~~"}


1	+ {"mean_reward": 1599.8339451418492, "std_reward": 71.5186359653171, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-23T10:34:30.002578"}

vec_normalize.pkl CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d187c3ace100927d14189f7e63dac658337969ea6631e21f036b28686019351d
 size 2136

 version https://git-lfs.github.com/spec/v1
+oid sha256:fdf509e67593f065f794aabde7f8d34f791c408a975edf03dc3bc00f585661a9
 size 2136