Initial commit

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 319.91 +/- 45.37
+      value: 505.92 +/- 61.06
       name: mean_reward
     task:
       type: reinforcement-learning

a2c-AntBulletEnv-v0.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8d29e28a2ad9d57ab69917d65be0b5ed487a51b4a980d9926509422ddbc1d02f
-size 124978
+oid sha256:237b942f18b09e3807cacf38b3172fa0a81d4781854285eab320c5db5dccc5ae
+size 129102

a2c-AntBulletEnv-v0/data

@@ -4,19 +4,19 @@
         ":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 0x7f40275aedc0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f40275aee50>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f40275aeee0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f40275aef70>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f40275b2040>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f40275b20d0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f40275b2160>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f40275b21f0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f40275b2280>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f40275b2310>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f40275b23a0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7ff4a3975dc0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff4a3975e50>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff4a3975ee0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff4a3975f70>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ff4a3978040>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ff4a39780d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff4a3978160>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ff4a39781f0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff4a3978280>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff4a3978310>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff4a39783a0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f40276a8d00>"
+        "_abc_impl": "<_abc._abc_data object at 0x7ff4a3ab4f40>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -58,12 +58,12 @@
         "_np_random": null
     },
     "n_envs": 4,
-    "num_timesteps": 224,
-    "_total_timesteps": 200,
+    "num_timesteps": 200064,
+    "_total_timesteps": 200000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1659066388.5388026,
+    "start_time": 1659070176.2426794,
     "learning_rate": 0.00096,
     "tensorboard_log": "work_dirs/AntBulletEnv-v0/tensorboard",
     "lr_schedule": {
@@ -72,7 +72,7 @@
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -80,22 +80,22 @@
     },
     "_last_original_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_episode_num": 0,
     "use_sde": true,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.1200000000000001,
+    "_current_progress_remaining": -0.000320000000000098,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+        ":serialized:": "gAWVLgwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHQIhnvlU6yMAWyUTegDjAF0lEdAXksFjd56dHV9lChoBkdAUeGFcpsoD2gHS3JoCEdAXl4A2hqTKXV9lChoBkdAftLP1L8JlmgHTegDaAhHQF6Tte2NNrV1fZQoaAZHQFovZTho/RpoB0uNaAhHQF63Qj2SMcZ1fZQoaAZHQE/Y5RTCLuRoB0upaAhHQF7hi6xxDLN1fZQoaAZHQIEI+BUaQ3hoB03oA2gIR0Be/Yc/+sHTdX2UKGgGR0B8OggeRxLkaAdN6ANoCEdAX0TL/0dzXHV9lChoBkdAeCisUIsyz2gHTegDaAhHQF9X2h7E5yV1fZQoaAZHQFFdDziCJ41oB0teaAhHQF9b7z06HTJ1fZQoaAZHQG49CvHLiddoB01MAmgIR0BfkFcyFfzCdX2UKGgGR0BSaCNXHR1HaAdL1GgIR0BfkUfozN2UdX2UKGgGR0BUQNC/oJRgaAdL62gIR0BfkqwD/2kBdX2UKGgGR0AHFYuCf6GhaAdLF2gIR0Bfla72+PBBdX2UKGgGRz//kuYhMajvaAdLFGgIR0Bfl4EOiFj/dX2UKGgGR0B48UPe54GEaAdN8QJoCEdAX50MkQf6oHV9lChoBkdAeE5ALy+YdGgHTegDaAhHQGBG2+wkgOl1fZQoaAZHQHdLsH8jzI5oB03oA2gIR0BgSVSl3yI6dX2UKGgGR0B2LFcIJJGwaAdN6ANoCEdAYEoqVhTfi3V9lChoBkdACTJQtSQ5m2gHSxRoCEdAYEu3gDRtxnV9lChoBkdAEfdTYNAkcGgHSxRoCEdAYEykhzNliHV9lChoBkdAfe7qH446wWgHTegDaAhHQGBNC7sfJV91fZQoaAZHQBLYR7JGOMloB0sVaAhHQGBPhQm/nGN1fZQoaAZHQDmR6w+t8u1oB0tAaAhHQGBTkzGgi/x1fZQoaAZHQF2gDmKZUkxoB0vIaAhHQGBfklme18d1fZQoaAZHQFTMCVKPGQ1oB0t0aAhHQGBuEPMB6rx1fZQoaAZHQHDu0gwGnoBoB00hAmgIR0BgkGPvKEFodX2UKGgGR0BwaqNaQmu1aAdNJgJoCEdAYJPjgAIY33V9lChoBkdAgVMdaEBbOmgHTYUDaAhHQGDDz6rNnoR1fZQoaAZHQH3R9M495hVoB03oA2gIR0Bg6vBFd9lVdX2UKGgGR0B/Debd8Aq/aAdN6ANoCEdAYQ1rhR64UnV9lChoBkdAEP/vv0AcUGgHSxRoCEdAYQ/V3EAHV3V9lChoBkdAfXLg+yJKrmgHTegDaAhHQGERF3pwCKd1fZQoaAZHQHR0nKGL1mJoB02mAmgIR0BhGL+R5kbxdX2UKGgGR0B7nIHX2/SIaAdN6ANoCEdAYWkOPNmlInV9lChoBkdAfL8S/TLGJmgHTQUDaAhHQGFxi5Etuk11fZQoaAZHQBU3kgfU4JhoB0sUaAhHQGFz6lDWsil1fZQoaAZHQHwlC/fwZwZoB03oA2gIR0BhjvJDE3sHdX2UKGgGR0B6WFeC04R3aAdN6ANoCEdAYZaZmZmZmnV9lChoBkdAZzEFkhA4XGgHTWsBaAhHQGGhHLaEi+t1fZQoaAZHQFKgnqmj0thoB0vCaAhHQGG5Zs9B8hN1fZQoaAZHQH+kp6+nIhhoB03oA2gIR0Bh5lJ17pmmdX2UKGgGR0BlAJtUGVzIaAdNFQJoCEdAYfx69CeEqXV9lChoBkdAeoBbp/wy7GgHTegDaAhHQGIMSlvZRKp1fZQoaAZHQHfQP9P1tfpoB03oA2gIR0BiFBDohY/3dX2UKGgGR0CAJ9TkyULVaAdN6ANoCEdAYmMr92ovSXV9lChoBkdAd6DiS7oStmgHTegDaAhHQGJ5eevpyIZ1fZQoaAZHQIJICkl/pdNoB03oA2gIR0BiiRO+IuXedX2UKGgGR0CAXyw+t8u0aAdN6ANoCEdAYpDJSR8tw3V9lChoBkdAYrUK1og3cmgHTSEBaAhHQGK0lERaouR1fZQoaAZHQG8DBVlwtJ5oB03jAWgIR0BitWnXNC7cdX2UKGgGR0BTLQHzH0btaAdLX2gIR0BiwR17pmmMdX2UKGgGR0BZwJXdTHbRaAdLr2gIR0Biyjt1IRRNdX2UKGgGR0CAV2iDdxhlaAdN6ANoCEdAYt+u4gA6uHV9lChoBkdAgFLM/Y8MeGgHTegDaAhHQGMF0lJHy3F1fZQoaAZHQHTB854nndRoB01DAmgIR0BjEstNBWxRdX2UKGgGR0CC11QD3dsSaAdN6ANoCEdAYz5NahYeT3V9lChoBkdAgfboYWLxZ2gHTegDaAhHQGNcsjFAE+x1fZQoaAZHQIGt6uW8h9toB03oA2gIR0BjgwGr0aqCdX2UKGgGR0CBb4rRSgoPaAdN6ANoCEdAY4/7N0NjLHV9lChoBkdAgvXJokAxSGgHTegDaAhHQGO64u9OARV1fZQoaAZHQIJuE2FWXC1oB03oA2gIR0Bj2UKzAvcrdX2UKGgGR0CCoyuXeFcqaAdN6ANoCEdAY/8JUo8ZDXV9lChoBkdAhTsGDUVi4WgHTegDaAhHQGQMBw2l2vB1fZQoaAZHQH3sS8WbgCRoB03oA2gIR0BkNyzmfXf7dX2UKGgGR0CBkwEMb3oLaAdN6ANoCEdAZFV3pwCKaXV9lChoBkdAgWgjbzshPmgHTegDaAhHQGR7euNgjQl1fZQoaAZHQFDDywOe8PFoB0tSaAhHQGSFypiqhlF1fZQoaAZHQIF0phhH9WJoB03oA2gIR0BkiKlBQemvdX2UKGgGR0CARvEgntv5aAdN6ANoCEdAZLSxASnLq3V9lChoBkdAgWl9AgPmP2gHTegDaAhHQGTT5dGAkLR1fZQoaAZHQIRSeQ6p5u9oB03oA2gIR0BlBVtqHoHLdX2UKGgGR0B68iI1tO2zaAdN6ANoCEdAZQg/JvHcUXV9lChoBkdAgVpdbor4FmgHTegDaAhHQGUzjRlYlpp1fZQoaAZHQIGsjH0btJFoB03oA2gIR0BlUcwtapxWdX2UKGgGR0CB9GuDBdleaAdN6ANoCEdAZYJpGnXNDHV9lChoBkdAgYGekxh2GWgHTegDaAhHQGWFIddVvMt1fZQoaAZHQIL8xhx5s0poB03oA2gIR0BlsGFQEZBLdX2UKGgGR0B+AOjZcs19aAdN6ANoCEdAZc8IY3vQW3V9lChoBkdAgSbaya/h2mgHTegDaAhHQGX/N8NQTEl1fZQoaAZHQIAHbTQVsUJoB03oA2gIR0BmAe8scyWSdX2UKGgGR0CBAJwiJO32aAdN6ANoCEdAZi0HPeHi33V9lChoBkdAg77J/wy6+WgHTegDaAhHQGZMAM+eOGV1fZQoaAZHQHxI8yeqaPVoB00VA2gIR0BmYcYMvyskdX2UKGgGR0CEp9ML4N7TaAdN6ANoCEdAZn7ronrpq3V9lChoBkdAerxAE+xGD2gHTY8DaAhHQGafDdYW+Gp1fZQoaAZHQH0SffCQ9zRoB03oA2gIR0Bmx/Efkmx/dX2UKGgGR0CBJZVS4vvjaAdN6ANoCEdAZtzb349HMHV9lChoBkdAXxQ5DJEH+2gHS/loCEdAZuWHgxagVXV9lChoBkdAUMtvcafjCGgHS4loCEdAZvWVSGahH3V9lChoBkdAghecWKuSwGgHTegDaAhHQGb4VQhwEQp1fZQoaAZHQIHKgM+eOGVoB03oA2gIR0BnFyFM7EHddX2UKGgGR0CEMqxASnLraAdN6ANoCEdAZ1RBF/hESnV9lChoBkdAf94JqqOtGWgHTegDaAhHQGduAl4TsY51fZQoaAZHQHmh7j94u9RoB03oA2gIR0BncKMcZLqVdX2UKGgGR0CBvKOo5xR3aAdN6ANoCEdAZ48SmIj4YnV9lChoBkdAgVDESElE7WgHTegDaAhHQGfLm65Gz8h1fZQoaAZHQIBXxt52QnxoB03oA2gIR0Bn5H4ubqhUdX2UKGgGR0CFSOAMDwH8aAdN6ANoCEdAZ+cY1pCa7XV9lChoBkdAgtroUrTYumgHTegDaAhHQGgFoQnQY1p1fZQoaAZHQIN4B/LDAJtoB03oA2gIR0BoQevIOpbVdWUu"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 7,
-    "n_steps": 8,
+    "_n_updates": 1563,
+    "n_steps": 32,
     "gamma": 0.99,
     "gae_lambda": 0.9,
     "ent_coef": 0.0,

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4ee66febc94a8ec689d14258ae684b21855ee351922fec3367834632a3455b51
+oid sha256:fb60d89787f599c1b34148ec18850480f184c022058176468f70e6287635973e
 size 56126

a2c-AntBulletEnv-v0/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8239193c6dd3e0b46a140ee94bdcc05d88b9e2ff83160feea3630f29241efb22
+oid sha256:4b22544a66ceb807c80cdefeb444d32a9c086f0bb43153a9d5f6caa926c6ecd5
 size 56766

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 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 0x7f40275aedc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f40275aee50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f40275aeee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f40275aef70>", "_build": "<function ActorCriticPolicy._build at 0x7f40275b2040>", "forward": "<function ActorCriticPolicy.forward at 0x7f40275b20d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f40275b2160>", "_predict": "<function ActorCriticPolicy._predict at 0x7f40275b21f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f40275b2280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f40275b2310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f40275b23a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f40276a8d00>"}, "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": 224, "_total_timesteps": 200, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1659066388.5388026, "learning_rate": 0.00096, "tensorboard_log": "work_dirs/AntBulletEnv-v0/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": -0.1200000000000001, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 7, "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-5.4.0-122-generic-x86_64-with-glibc2.27 #138~18.04.1-Ubuntu SMP Fri Jun 24 14:14:03 UTC 2022", "Python": "3.9.12", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.0+cu102", "GPU Enabled": "True", "Numpy": "1.23.1", "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 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 0x7ff4a3975dc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff4a3975e50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff4a3975ee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff4a3975f70>", "_build": "<function ActorCriticPolicy._build at 0x7ff4a3978040>", "forward": "<function ActorCriticPolicy.forward at 0x7ff4a39780d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff4a3978160>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff4a39781f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff4a3978280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff4a3978310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff4a39783a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff4a3ab4f40>"}, "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": 200064, "_total_timesteps": 200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1659070176.2426794, "learning_rate": 0.00096, "tensorboard_log": "work_dirs/AntBulletEnv-v0/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": -0.000320000000000098, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 1563, "n_steps": 32, "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-5.4.0-122-generic-x86_64-with-glibc2.27 #138~18.04.1-Ubuntu SMP Fri Jun 24 14:14:03 UTC 2022", "Python": "3.9.12", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.0+cu102", "GPU Enabled": "True", "Numpy": "1.23.1", "Gym": "0.21.0"}}

logs/a2c-AntBulletEnv-v0.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:772228a9a4d9e086c1e92c5b33d520c8f12a6f2ac23b79e45a4477fa5d2980d9
-size 124978
+oid sha256:51cd1c8a3464d310036a2c7962a0e77e46a7345fe47854975d0c547da4252e54
+size 129102

logs/tensorboard/A2C_6/events.out.tfevents.1659070176.rlcube.3780.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4b46f245772945d81562fdc388644b890fd5836ad00aec787f37a270332e8cb
+size 7666

logs/vec_normalize.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c0b392508ce6c26f1cec6f21bc88fbf848e85a948614f37206e4a8a3f7f6ba80
+oid sha256:19bc7cfb7f32fa0d3b5f9184d6b324ce511cf835068efff345b2d57222e8eb30
 size 2453

results.json

@@ -1 +1 @@
-{"mean_reward": 319.91129219942377, "std_reward": 45.37349312855758, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-29T11:46:52.525094"}
+{"mean_reward": 505.92322014253585, "std_reward": 61.055729640207375, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-29T12:53:16.464708"}

vec_normalize.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d605634e782a19c6dc2ba98a0b93622262ea131a9e45f102715f5f9fc5a1f358
+oid sha256:916294162b3622e720f6fffe0642fc95258a340a0c11d16438c2869fde8e0d2d
 size 2521