2nd try

Browse files

Files changed (9) hide show

README.md +1 -1
a2c-AntBulletEnv-v0.zip +2 -2
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: 804.66 +/- 299.98
       name: mean_reward
       verified: false
 ---

       type: AntBulletEnv-v0
     metrics:
     - type: mean_reward
+      value: 1899.99 +/- 47.00
       name: mean_reward
       verified: false
 ---

a2c-AntBulletEnv-v0.zip CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2b24a707646ff2579f821c15e452a847e3287e39126e4517cbfed4fe7f276ecc
-size 129256

 version https://git-lfs.github.com/spec/v1
+oid sha256:5c7485dd7a71867d8f2bff91d8c8515562966fcb3a36e12746e51607376dc5a4
+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 0x7fc15a4af1f0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc15a4af280>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc15a4af310>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc15a4af3a0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7fc15a4af430>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7fc15a4af4c0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc15a4af550>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc15a4af5e0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7fc15a4af670>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc15a4af700>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc15a4af790>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc15a4af820>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fc15a4b0090>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -64,7 +64,7 @@
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1678227298335134457,
     "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 0x7f03cc0ef430>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f03cc0ef4c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f03cc0ef550>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f03cc0ef5e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f03cc0ef670>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f03cc0ef700>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f03cc0ef790>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f03cc0ef820>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f03cc0ef8b0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f03cc0ef940>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f03cc0ef9d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f03cc0efa60>",
         "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f03cc0eb810>"
     },
     "verbose": 1,
     "policy_kwargs": {
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
+    "start_time": 1678269101955792433,
     "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:c68fb517cf1f43cdf3a689b9a2d3fb07451c9a8094ed2df7e6eb49238ed4969f
 size 56190

 version https://git-lfs.github.com/spec/v1
+oid sha256:dc4470aaeb6aea6e77c04b7ffcd48334e800c86b3ee34b403624f85fd594c4fa
 size 56190

a2c-AntBulletEnv-v0/policy.pth CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c09eb4bd6eca9fd700accbc988df113b41c6f0edb0afc682f5e2bef524588092
 size 56958

 version https://git-lfs.github.com/spec/v1
+oid sha256:3c8878574a9d8ed917d05aed34ee4a4464adb52f04eaf6224427597a46c3fc03
 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 0x7fc15a4af1f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc15a4af280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc15a4af310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc15a4af3a0>", "_build": "<function ActorCriticPolicy._build at 0x7fc15a4af430>", "forward": "<function ActorCriticPolicy.forward at 0x7fc15a4af4c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc15a4af550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc15a4af5e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc15a4af670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc15a4af700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc15a4af790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc15a4af820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fc15a4b0090>"}, "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": 1678227298335134457, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/T3UQTVUdaYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_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": "1.22.4", "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 0x7f03cc0ef430>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f03cc0ef4c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f03cc0ef550>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f03cc0ef5e0>", "_build": "<function ActorCriticPolicy._build at 0x7f03cc0ef670>", "forward": "<function ActorCriticPolicy.forward at 0x7f03cc0ef700>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f03cc0ef790>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f03cc0ef820>", "_predict": "<function ActorCriticPolicy._predict at 0x7f03cc0ef8b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f03cc0ef940>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f03cc0ef9d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f03cc0efa60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f03cc0eb810>"}, "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": 1678269101955792433, "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": "1.22.4", "Gym": "0.21.0"}}

replay.mp4 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3208cf087ed96a5b2300f10a489efa584d5026311b1c6053da53b21ad077ccb1
-size 1041908

 version https://git-lfs.github.com/spec/v1
+oid sha256:1ecb3d901a7d1c7d76497b6c71c5b1fff8c7b8c92ca71ca930a84fbfe96c849e
+size 1187680

results.json CHANGED Viewed

	@@ -1 +1 @@
1	- {"mean_reward": ~~804~~.~~6593259568442~~, "std_reward": ~~299~~.~~97901707700447~~, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-~~07T23~~:17:41.~~551908~~"}


1	+ {"mean_reward": 1899.990873548691, "std_reward": 47.00396281005085, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-08T10:49:52.592862"}

vec_normalize.pkl CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1d316e9d01cb2a5bc2c7b0871f3b538c59ab21e62353f92d70eda9abb223a760
 size 2136

 version https://git-lfs.github.com/spec/v1
+oid sha256:f24b8bacb555ab7344a88038cd9d1982b73da1032cc7490f44e6ca0608d8e295
 size 2136