Initial commit

README.md

@@ -8,16 +8,17 @@ tags:
 model-index:
 - name: A2C
   results:
-  - metrics:
-    - type: mean_reward
-      value: 1070.49 +/- 165.67
-      name: mean_reward
-    task:
+  - task:
       type: reinforcement-learning
       name: reinforcement-learning
     dataset:
       name: AntBulletEnv-v0
       type: AntBulletEnv-v0
+    metrics:
+    - type: mean_reward
+      value: 801.44 +/- 64.36
+      name: mean_reward
+      verified: false
 ---
 
 # **A2C** Agent playing **AntBulletEnv-v0**

a2c-AntBulletEnv-v0.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1b47b06ea1b45243221acd07aeb95873799748f64c8a612a491804f278f73d7d
-size 129188
+oid sha256:b807671f4bbedae8bf3b0df121b25a64cc828ab4d25f623915ea81f90fef0f23
+size 129276

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -1 +1 @@
-1.6.0
+1.7.0

a2c-AntBulletEnv-v0/data

@@ -1,27 +1,28 @@
 {
     "policy_class": {
         ":type:": "<class 'abc.ABCMeta'>",
-        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        ":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 0x7f65561135f0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6556113680>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6556113710>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f65561137a0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f6556113830>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f65561138c0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6556113950>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f65561139e0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6556113a70>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6556113b00>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6556113b90>",
+        "__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 0x7f6855672670>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6855672700>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6855672790>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6855672820>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f68556728b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f6855672940>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f68556729d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6855672a60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f6855672af0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6855672b80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6855672c10>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6855672ca0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f655615d930>"
+        "_abc_impl": "<_abc_data object at 0x7f68556d8bd0>"
     },
     "verbose": 1,
     "policy_kwargs": {
         ":type:": "<class 'dict'>",
-        ":serialized:": "gASVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
+        ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
         "log_std_init": -2,
         "ortho_init": false,
         "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
@@ -33,7 +34,7 @@
     },
     "observation_space": {
         ":type:": "<class 'gym.spaces.box.Box'>",
-        ":serialized:": "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",
+        ":serialized:": "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",
         "dtype": "float32",
         "_shape": [
             28
@@ -46,7 +47,7 @@
     },
     "action_space": {
         ":type:": "<class 'gym.spaces.box.Box'>",
-        ":serialized:": "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",
+        ":serialized:": "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",
         "dtype": "float32",
         "_shape": [
             8
@@ -58,43 +59,43 @@
         "_np_random": null
     },
     "n_envs": 4,
-    "num_timesteps": 2000000,
+    "num_timesteps": 1389188,
     "_total_timesteps": 2000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1658413461.081193,
+    "start_time": 1673896273274332155,
     "learning_rate": 0.00096,
     "tensorboard_log": "./tensorboard",
     "lr_schedule": {
         ":type:": "<class 'function'>",
-        ":serialized:": "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"
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/T3UQTVUdaYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAAAAACUdJRiLg=="
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
     },
     "_last_original_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "gASVTQIAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwRLHIaUaAOMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiiULAAQAAAAAAAHw1MzYAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAIB1m+E7AAAAAIoG5b8AAAAAdCDNPQAAAAAgBgBAAAAAABupE70AAAAA9grlPwAAAAAWXGC9AAAAAOEu+b8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABN1KG1AACAPwAAAAAAAAAAAAAAAAAAAAAAAACAPno0PAAAAABAp+G/AAAAADw+pr0AAAAAOw76PwAAAAAW6P49AAAAAIB69j8AAAAA06yhPQAAAACy3Pu/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAArxiQtgAAgD8AAAAAAAAAAAAAAAAAAAAAAAAAgCsjzT0AAAAAuSj7vwAAAAAQrlA8AAAAACIB/D8AAAAAMj1CuwAAAACK++o/AAAAAKgCwz0AAAAACZDcvwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMgvrTYAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAICu3re9AAAAAB/v5r8AAAAArWSCvQAAAAAFUvs/AAAAAA1N9T0AAAAAyZkAQAAAAABdIaS9AAAAALCB578AAAAAAAAAAAAAAAAAAAAAAAAAAJR0lGIu"
+        ":serialized:": "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"
     },
     "_episode_num": 0,
     "use_sde": true,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": 0.0,
+    "_current_progress_remaining": 0.305408,
     "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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 62500,
+    "_n_updates": 43412,
     "n_steps": 8,
     "gamma": 0.99,
     "gae_lambda": 0.9,

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:91e28ceb90c9f3f70622068eaec980e91f417669cae23af78080fb4f21cbf66c
-size 56126
+oid sha256:6576f918a6cf9fb6bf52281d04df7708082bdd99e466eb1bd3bd6ebedc35bb49
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9104491f441b2befc476e564807cf7734699a443351c557ead0123d5ad740703
-size 56766
+oid sha256:3c0764bbd10a1c9ff003100c1610f37379a75f76715f3c7e14f048768d62dc6e
+size 56958

a2c-AntBulletEnv-v0/system_info.txt

@@ -1,7 +1,7 @@
-OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
-Python: 3.7.13
-Stable-Baselines3: 1.6.0
-PyTorch: 1.12.0+cu113
-GPU Enabled: True
-Numpy: 1.21.6
-Gym: 0.21.0
+- OS: Linux-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.0+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f65561135f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6556113680>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6556113710>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f65561137a0>", "_build": "<function ActorCriticPolicy._build at 0x7f6556113830>", "forward": "<function ActorCriticPolicy.forward at 0x7f65561138c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6556113950>", "_predict": "<function ActorCriticPolicy._predict at 0x7f65561139e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6556113a70>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6556113b00>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6556113b90>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f655615d930>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gASVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/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": 1658413461.081193, "learning_rate": 0.00096, "tensorboard_log": "./tensorboard", "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVTQIAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwRLHIaUaAOMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiiULAAQAAoqKWPqkv4z8418G/dCycPpneJj83b0I///6IPakXY77VI+8+PTo+PdXxET+2Lqw+kLmgvmxetr/rJwy9dDyTv/yrVz/G7D6/X4e1vh4y7D48qie/sWyXvB4g1z7ivNO+bCh0P3CpAz+4MA8/cmV7P3EEDb/CNay/6xpPvgOKBD+BqHs/3OIUP4OS/T56+5S+0RMoP3X5I75E8pE+vukKvtlUp791hRo/0Qr2PbGRsL8oef+7EOrOPruqZj/6kNA9hfxjPhzXDUBRmhe/yrlPP2wodD9E4fi/uDAPPxFYgr/ixQrAjrGmP+GN1L6sQtK/K9jLvORbEjo2ABc+zfl3P7LTIT+u1B6+AgRrvwpqcTzkcHc/0H76vvQ0HD/cNQI//Zvav2HAib233kY/5n88PkGlm76XJcw+Y2+Xv35LhrpsKHQ/cKkDP7gwDz9yZXs/Hsn7PhZoA0DCBTDArvnNPtXzUL+O2xu/tldePfdebb+8eSU/DdA7P80Isz32Zre+9u1NPlF2jz4/teI+RfwXPjVZnD/Do7a8LfLrvlSHpr1aqOm+3NXmP/YkfD9liWS+TjWGv3CpAz+4MA8/cmV7P5R0lGIu"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAAAAACUdJRiLg=="}, "_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:": "gASVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQI2nddeIEbKMAWyUTegDjAF0lEdAp5C4KD0163V9lChoBkdAnzjggHNX5mgHTegDaAhHQKeRiu0TlDF1fZQoaAZHQHY60qlP8AJoB03oA2gIR0Cnk8tYB/7SdX2UKGgGR0CJWu0Mw1ziaAdN6ANoCEdAp5pwtjCpFXV9lChoBkdAko0v2oNutWgHTegDaAhHQKedfzMA3kx1fZQoaAZHQJwU7RCx/utoB03oA2gIR0CnnkrIo3JgdX2UKGgGR0Cc5coLG7z1aAdN6ANoCEdAp6CEYZVGTnV9lChoBkdAhq9ZU1hsqWgHTegDaAhHQKenLot+TeR1fZQoaAZHQIyjDkKeCkJoB03oA2gIR0CnqjIFmnO0dX2UKGgGR0CWbSjEvTPTaAdN6ANoCEdAp6sB42S+xnV9lChoBkdAmce+jVQQ+WgHTegDaAhHQKetLY6GQCF1fZQoaAZHQIXgFpyp71JoB03oA2gIR0Cns/S26TW5dX2UKGgGR0CH5QZWJaaDaAdN6ANoCEdAp7b/1tfoinV9lChoBkdAjsl0/fO2RmgHTegDaAhHQKe3yzvZyuJ1fZQoaAZHQJKawqhDgIhoB03oA2gIR0CnufddVvMsdX2UKGgGR0CHmfqoqCpWaAdN6ANoCEdAp8CBWNm16XV9lChoBkdAkaTW7nPmgmgHTegDaAhHQKfDfiXpnpV1fZQoaAZHQJ8rwWN3np1oB03oA2gIR0CnxEbMHKOldX2UKGgGR0CciK6H0se5aAdN6ANoCEdAp8aL0UXYUXV9lChoBkdAlnpqLKmsNmgHTegDaAhHQKfNC8zyjHp1fZQoaAZHQI4n5uhsZYRoB03oA2gIR0Cn0Asg2ZRbdX2UKGgGR0Ca23NbC79RaAdN6ANoCEdAp9DTN8ma6XV9lChoBkdAne6eI2wV02gHTegDaAhHQKfS7kupS751fZQoaAZHQJSCikcjqwBoB03oA2gIR0Cn2ZNg0CRwdX2UKGgGR0CUwzBUJfICaAdN6ANoCEdAp9yZeiSJTHV9lChoBkdAmus9YOlO5GgHTegDaAhHQKfdXQrMC911fZQoaAZHQJih2Axzq8loB03oA2gIR0Cn33i2MKkVdX2UKGgGR0Cc0ncafjCIaAdN6ANoCEdAp+XuAkLQX3V9lChoBkdAmItv51vETGgHTegDaAhHQKfo6bkwN9Z1fZQoaAZHQJeyqoBJZntoB03oA2gIR0Cn6bSVObiIdX2UKGgGR0Camsru6VdHaAdN6ANoCEdAp+vsGA08/3V9lChoBkdAlfTPNiYsumgHTegDaAhHQKfyatYB/7V1fZQoaAZHQJRAsYyfthNoB03oA2gIR0Cn9VBDPWxydX2UKGgGR0CWpvflp48maAdN6ANoCEdAp/YWxdIGyHV9lChoBkdAmKc7bHp8nmgHTegDaAhHQKf4V/4qPOp1fZQoaAZHQJ+bXV/c32poB03oA2gIR0Cn/skpy6tldX2UKGgGR0CeeuDdP+GXaAdN6ANoCEdAqAHDKPn0TXV9lChoBkdAnn7SlabF0mgHTegDaAhHQKgCkLuQZGd1fZQoaAZHQJxCaOhkAghoB03oA2gIR0CoBNjxLCemdX2UKGgGR0CdE/VrRBu5aAdN6ANoCEdAqAtYlfJFLHV9lChoBkdAmTqP/BFd9mgHTegDaAhHQKgOUMcZLqV1fZQoaAZHQJy15YbKifxoB03oA2gIR0CoDx46GQCCdX2UKGgGR0Caz9U8V58jaAdN6ANoCEdAqBFUr/bTMXV9lChoBkdAn3FAQxveg2gHTegDaAhHQKgXqHSnccl1fZQoaAZHQJrNi+sYEW9oB03oA2gIR0CoGprNnoPkdX2UKGgGR0CcyfXEZR8/aAdN6ANoCEdAqBti0Sh8IHV9lChoBkdAnScaAOJ+D2gHTegDaAhHQKgdeXOW0JF1fZQoaAZHQJ/l6JO32EloB03oA2gIR0CoI/HlwLmZdX2UKGgGR0CgWaz6ab4KaAdN6ANoCEdAqCbKFVT723V9lChoBkdAnj3eoHcDbWgHTegDaAhHQKgnkgi/wiJ1fZQoaAZHQJ3X0mw7kn1oB03oA2gIR0CoKdDAaef7dX2UKGgGR0Cfh8xsEaESaAdN6ANoCEdAqDA2+fywwHV9lChoBkdAnrggZKnNxGgHTegDaAhHQKgzJHAh0Qt1fZQoaAZHQJ9XCYlY2bZoB03oA2gIR0CoM/T/IbOvdX2UKGgGR0Ce9nG1x82KaAdN6ANoCEdAqDYmnuRcNnV9lChoBkdAnvaBpHqeLGgHTegDaAhHQKg8g1twaR91fZQoaAZHQJ4SqpHZsbhoB03oA2gIR0CoP3NuUD+zdX2UKGgGR0CdUFB68g6maAdN6ANoCEdAqEBOBOHnEHV9lChoBkdAmUYMjVx0dWgHTegDaAhHQKhCeTWXkYJ1fZQoaAZHQJwTZtALRa5oB03oA2gIR0CoSOdQ40djdX2UKGgGR0CcU8L2YfGNaAdN6ANoCEdAqEvN8ohIOHV9lChoBkdAnOlbuc+aB2gHTegDaAhHQKhMmfq5byJ1fZQoaAZHQJdLf7P6bfBoB03oA2gIR0CoTrkBjnV5dX2UKGgGR0CcVnGi5/b1aAdN6ANoCEdAqFUtpqREGHV9lChoBkdAlu7Ytg8bJmgHTegDaAhHQKhYIIfr8ix1fZQoaAZHQJa4e3I+4b1oB03oA2gIR0CoWOqMWGh3dX2UKGgGR0CXv6VGTcIraAdN6ANoCEdAqFsbTQVsUXV9lChoBkdAmfPIi9qUNmgHTegDaAhHQKhhkQV9F4N1fZQoaAZHQJZ4SXjU/fRoB03oA2gIR0CoZIw8GLUDdX2UKGgGR0CYRUIqLCN0aAdN6ANoCEdAqGVUXN1QqXV9lChoBkdAlrUc7uDzy2gHTegDaAhHQKhnet5D7ZZ1fZQoaAZHQJNNUHAymANoB03oA2gIR0CobeGi5/b1dX2UKGgGR0CX2tC2MKkVaAdN6ANoCEdAqHDX863iJnV9lChoBkdAk7/qBNEgGWgHTegDaAhHQKhxoKpDNQl1fZQoaAZHQJa4Iv/R3NdoB03oA2gIR0Coc775Ec81dX2UKGgGR0CeDDyhzvJBaAdN6ANoCEdAqHolPN3W4HV9lChoBkdAmKqMr3CbdGgHTegDaAhHQKh9IFcIJJJ1fZQoaAZHQJpFxXZGrjpoB03oA2gIR0Cofezb349HdX2UKGgGR0CXkN3BYV7AaAdN6ANoCEdAqIAmQU5+6XV9lChoBkdAn2SBXS0BwWgHTegDaAhHQKiGmenyd4F1fZQoaAZHQJ1uqpPykKxoB03oA2gIR0CoiXe3x4IKdX2UKGgGR0CfgS/UvwmWaAdN6ANoCEdAqIpD0aqCH3V9lChoBkdAm4OD63y7PWgHTegDaAhHQKiMb/VAiV11fZQoaAZHQJhhD4pMHr1oB03oA2gIR0Cokq4f4h2XdX2UKGgGR0CVvrd5IH1OaAdN6ANoCEdAqJWXA/LTyHV9lChoBkdAnTiuVTrE+GgHTegDaAhHQKiWWm3vx6R1fZQoaAZHQJllUczZYgdoB03oA2gIR0ComHONHYpVdX2UKGgGR0CcswIrOJLvaAdN6ANoCEdAqJ7DjcVQAXV9lChoBkdAoQrsANoak2gHTegDaAhHQKihr987ZFp1fZQoaAZHQJkUjpt78eloB03oA2gIR0CoonMJx//edX2UKGgGR0CeT0WHDaXbaAdN6ANoCEdAqKSGZ9d/rnV9lChoBkdAoFLyYRdyDWgHTegDaAhHQKiq3a37UG51fZQoaAZHQJs4kR6F/QVoB03oA2gIR0Corc7Lt/nXdX2UKGgGR0Cc+KICEHt4aAdN6ANoCEdAqK6TB2wFDHV9lChoBkdAoJyDs0HhTGgHTegDaAhHQKiwtepGWld1fZQoaAZHQKBTip3HJcRoB03oA2gIR0CotwwKjSG8dX2UKGgGR0CVaUhH9WIXaAdN6ANoCEdAqLn9hPTG53V9lChoBkdAoLYrl1bJOmgHTegDaAhHQKi6w6UaAFx1fZQoaAZHQJA3P4L1EmZoB03oA2gIR0CovN6oVEeAdX2UKGgGR0CUP9Mqz7djaAdN6ANoCEdAqMNWMju8b3VlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.0+cu113", "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 0x7f6855672670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6855672700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6855672790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6855672820>", "_build": "<function ActorCriticPolicy._build at 0x7f68556728b0>", "forward": "<function ActorCriticPolicy.forward at 0x7f6855672940>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f68556729d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6855672a60>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6855672af0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6855672b80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6855672c10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6855672ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f68556d8bd0>"}, "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": 1389188, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1673896273274332155, "learning_rate": 0.00096, "tensorboard_log": "./tensorboard", "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": 0.305408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 43412, "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-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -1 +1 @@
-{"mean_reward": 1070.4934912229771, "std_reward": 165.66638735349437, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-21T15:39:07.525547"}
+{"mean_reward": 801.4429149741554, "std_reward": 64.35514549740704, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-16T19:49:02.589210"}

vec_normalize.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:885b0ff3b17d5a071ea4d55c34e4fe032a5ee362753b808cb49e80811ec6d83e
-size 2763
+oid sha256:52aee4c2618a48d3d9996e0bec652f8a110be44fb8b8409645a65c5e8efc7193
+size 2521