2k timesteps

README.md

@@ -1,7 +1,7 @@
 ---
 library_name: stable-baselines3
 tags:
-- AntBulletEnv-v0
+- HalfCheetahBulletEnv-v0
 - deep-reinforcement-learning
 - reinforcement-learning
 - stable-baselines3
@@ -10,18 +10,18 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 490.34 +/- 55.67
+      value: 1609.67 +/- 949.14
       name: mean_reward
     task:
       type: reinforcement-learning
       name: reinforcement-learning
     dataset:
-      name: AntBulletEnv-v0
-      type: AntBulletEnv-v0
+      name: HalfCheetahBulletEnv-v0
+      type: HalfCheetahBulletEnv-v0
 ---
 
-# **A2C** Agent playing **AntBulletEnv-v0**
-This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
+# **A2C** Agent playing **HalfCheetahBulletEnv-v0**
+This is a trained model of a **A2C** agent playing **HalfCheetahBulletEnv-v0**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
 ## Usage (with Stable-baselines3)

a2c-HalfCheetahBulletEnv-v0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e01554929484e3d68b904edcfd09824789b79605a3b2759a85fc30bcfef04bd6
+size 124887

a2c-HalfCheetahBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.0

a2c-HalfCheetahBulletEnv-v0/data

@@ -0,0 +1,105 @@
+{
+    "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 0x7eff0b61f170>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7eff0b61f200>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7eff0b61f290>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7eff0b61f320>",
+        "_build": "<function ActorCriticPolicy._build at 0x7eff0b61f3b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7eff0b61f440>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7eff0b61f4d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7eff0b61f560>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7eff0b61f5f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7eff0b61f680>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7eff0b61f710>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7eff0b6741e0>"
+    },
+    "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": [
+            26
+        ],
+        "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]",
+        "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]",
+        "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]",
+        "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]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            6
+        ],
+        "low": "[-1. -1. -1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1. 1. 1.]",
+        "bounded_below": "[ True  True  True  True  True  True]",
+        "bounded_above": "[ 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": 1663171323.9059737,
+    "learning_rate": 0.00095,
+    "tensorboard_log": "./tensorboard",
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gASVwQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuAQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP08hLXcxj8WFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAEBAQGUdJRiLg=="
+    },
+    "_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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 62500,
+    "n_steps": 8,
+    "gamma": 0.992,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.4,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

a2c-HalfCheetahBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37cecbb65831f1f060d8da7764810242d5c6f92fcdc91d2422d04049249f1bc1
+size 54078

a2c-HalfCheetahBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:54a038fd37acf0c25e5485e88052605c3da66a9466a55269011ac46f0e4514fd
+size 54718

a2c-HalfCheetahBulletEnv-v0/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-HalfCheetahBulletEnv-v0/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.7.13
+Stable-Baselines3: 1.6.0
+PyTorch: 1.12.1+cu113
+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 0x7fef6f41e680>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fef6f41e710>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fef6f41e7a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fef6f41e830>", "_build": "<function ActorCriticPolicy._build at 0x7fef6f41e8c0>", "forward": "<function ActorCriticPolicy.forward at 0x7fef6f41e950>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fef6f41e9e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fef6f41ea70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fef6f41eb00>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fef6f41eb90>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fef6f41ec20>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fef6f4733c0>"}, "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": 15000000, "_total_timesteps": 15000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1662981387.6141422, "learning_rate": 0.00095, "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:": "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:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 468750, "n_steps": 8, "gamma": 0.992, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.7.13", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
+{"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 0x7eff0b61f170>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7eff0b61f200>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7eff0b61f290>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7eff0b61f320>", "_build": "<function ActorCriticPolicy._build at 0x7eff0b61f3b0>", "forward": "<function ActorCriticPolicy.forward at 0x7eff0b61f440>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7eff0b61f4d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7eff0b61f560>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7eff0b61f5f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7eff0b61f680>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7eff0b61f710>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7eff0b6741e0>"}, "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": [26], "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]", "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]", "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]", "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]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [6], "low": "[-1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True  True  True  True  True  True]", "bounded_above": "[ 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": 1663171323.9059737, "learning_rate": 0.00095, "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:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAEBAQGUdJRiLg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVLQIAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwRLGoaUaAOMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiiUKgAQAAAAAAAAAAAAAAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAIAwdtq+AAAAALWzCL0AAAAAIGefvgAAAAB6tqM+AAAAAEukyTwAAAAAgyqkPwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgD8AAAAAAAAAAAAAAAAAAAAAAAAAgJdVwr4AAAAA6ruWvQAAAAB1/dC+AAAAADCoiD4AAAAAWeCuPQAAAADdbZo/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAPwAAAAAAAAAAAAAAAAAAAAAAAACA2L7qvgAAAACbHEg9AAAAACS0xb4AAAAA7FSMPgAAAAD1IhY9AAAAAKwXoT8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAICof4y+AAAAABbPa7wAAAAA0Z/qvgAAAABCU5I+AAAAAOD067wAAAAAPSydPwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.992, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.7.13", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

replay.mp4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ef99a08713dac8042ad3c95330d94d98a7d293a5e5c5ebc21f9753acaa55e96c
-size 465438
+oid sha256:0ecb20857032e5ce4fed7f275e033ac7f4c6f15ed8bd3880e80d01fe80ecbfa4
+size 1221555

results.json

@@ -1 +1 @@
-{"mean_reward": 490.33812885251825, "std_reward": 55.67113434056032, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-09-12T18:33:44.572360"}
+{"mean_reward": 1609.6683122321963, "std_reward": 949.1374811506043, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-09-14T17:00:25.469478"}

vec_normalize.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:42fa20f071a536336c6d8cafeb3343f536692fc0ecdb6d9fcfe312e488585d17
-size 2763
+oid sha256:c9b6bf8a1fc8c375ba9b15068c6d0bab0ffd6746cef732782ddd492cb2ecd23e
+size 2659