Initial commit

.gitattributes

@@ -30,3 +30,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- HalfCheetahBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 1281.10 +/- 84.05
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: HalfCheetahBulletEnv-v0
+      type: HalfCheetahBulletEnv-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)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

a2c-HalfCheetahBulletEnv-v0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3ceef7d6987848092b580e3ab5cb1fcd5d01c542edb17f7d9157bf42d3dda2e
+size 124788

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:": "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 0x7f1a482de8b0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1a482de940>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1a482de9d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1a482dea60>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f1a482deaf0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f1a482deb80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1a482dec10>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f1a482deca0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1a482ded30>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1a482dedc0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1a482dee50>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f19987f9380>"
+    },
+    "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": [
+            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:": "gAWViwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLBoWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWGAAAAAAAAAAAAIC/AACAvwAAgL8AAIC/AACAvwAAgL+UaApLBoWUjAFDlHSUUpSMBGhpZ2iUaBIolhgAAAAAAAAAAACAPwAAgD8AAIA/AACAPwAAgD8AAIA/lGgKSwaFlGgVdJRSlIwNYm91bmRlZF9iZWxvd5RoEiiWBgAAAAAAAAABAQEBAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLBoWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYGAAAAAAAAAAEBAQEBAZRoIUsGhZRoFXSUUpSMCl9ucF9yYW5kb22UTnViLg==",
+        "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": 1663208743.8389914,
+    "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:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": 0.98,
+    "gae_lambda": 0.89,
+    "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:a4c0c4270e5f64582b1080f8679c72030d29f67b7bf4c89a1f4bde30c6df4bf1
+size 54078

a2c-HalfCheetahBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:05ec83e55e431ed95e669558b4bb3b986d7b0d79a03d08d4dcab2ba2f0fb4104
+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-5.15.57.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 #1 SMP Wed Jul 27 02:20:31 UTC 2022
+Python: 3.9.13
+Stable-Baselines3: 1.6.0
+PyTorch: 1.12.1
+GPU Enabled: True
+Numpy: 1.23.1
+Gym: 0.21.0

config.json

@@ -0,0 +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 0x7f1a482de8b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1a482de940>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1a482de9d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1a482dea60>", "_build": "<function ActorCriticPolicy._build at 0x7f1a482deaf0>", "forward": "<function ActorCriticPolicy.forward at 0x7f1a482deb80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1a482dec10>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1a482deca0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1a482ded30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1a482dedc0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1a482dee50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f19987f9380>"}, "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": [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": 1663208743.8389914, "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:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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:": "gAWVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQIWuRC8e0XyMAWyUTegDjAF0lEdArQ1V3ljmS3V9lChoBkdAh/tDNpudgGgHTegDaAhHQK0NVeenQ6Z1fZQoaAZHQISK5Z0Syt5oB03oA2gIR0CtDVX6yjYadX2UKGgGR0CFG2chkiD/aAdN6ANoCEdArQ1Vwo9cKXV9lChoBkdAhiPNZmqYJGgHTegDaAhHQK0dALZzxPR1fZQoaAZHQIGI7jYI0IloB03oA2gIR0CtHQCgK4QSdX2UKGgGR0CEfJC53C9AaAdN6ANoCEdArR0AomXw9nV9lChoBkdAddWudf9gnmgHTegDaAhHQK0dAGFBY3h1fZQoaAZHQJBdgpc5bQloB03oA2gIR0CtLAy57PY4dX2UKGgGR0CCaSa5PM0QaAdN6ANoCEdArSwMpTdcjnV9lChoBkdAhASjzZpSJmgHTegDaAhHQK0sDKmsNlR1fZQoaAZHQIlBsyP+4spoB03oA2gIR0CtLAxnWattdX2UKGgGR0CQBq+4LCvYaAdN6ANoCEdArTutsenyeHV9lChoBkdAgf+NR3u/lGgHTegDaAhHQK07rgTAWSF1fZQoaAZHQIEQbteD3/RoB03oA2gIR0CtO65xiobXdX2UKGgGR0CQUzEDhcZ+aAdN6ANoCEdArTuukSElFHV9lChoBkdAixBVrIo3JmgHTegDaAhHQK1LH0OEug91fZQoaAZHQI2KOnsLORloB03oA2gIR0CtSx8yFfzCdX2UKGgGR0CNUgZ4wAU+aAdN6ANoCEdArUsfQa72+XV9lChoBkdAiI5otcv/R2gHTegDaAhHQK1LHwR5C4V1fZQoaAZHQIcTGoWHk95oB03oA2gIR0CtWiYFaB7NdX2UKGgGR0CQNA08eS0TaAdN6ANoCEdArVom3fAKv3V9lChoBkdAh7+Rc/t6X2gHTegDaAhHQK1aJ9Nvfj11fZQoaAZHQIzHrrJKaodoB03oA2gIR0CtWih9Tgl4dX2UKGgGR0CPYNIq9XcQaAdN6ANoCEdArWkQ/5ckdHV9lChoBkdAiaB32ugYg2gHTegDaAhHQK1pEPy08eV1fZQoaAZHQI7SIAQxvehoB03oA2gIR0CtaREZzgdfdX2UKGgGR0CR09kzXSSeaAdN6ANoCEdArWkQ8EFGG3V9lChoBkdAjZXRiobXH2gHTegDaAhHQK14Ur5IpYt1fZQoaAZHQIs9td7fHghoB03oA2gIR0CteFKv/zasdX2UKGgGR0CHEV8KohpyaAdN6ANoCEdArXhSt9x6wHV9lChoBkdAi8zSdOIqLGgHTegDaAhHQK14UngHeJp1fZQoaAZHQI4WvvYvnKZoB03oA2gIR0CtiAHpSrHVdX2UKGgGR0CQSzT6i0v5aAdN6ANoCEdArYgB1s+FDnV9lChoBkdAkKWuieumrWgHTegDaAhHQK2IAdyT6i11fZQoaAZHQJBrh7mdRSBoB03oA2gIR0CtiAGYSg5BdX2UKGgGR0CN5xV7x/d7aAdN6ANoCEdArZbpMYdhiXV9lChoBkdAkPlZul41P2gHTegDaAhHQK2W6SowVTJ1fZQoaAZHQJJnzFYMfA9oB03oA2gIR0CtlukxREWqdX2UKGgGR0CRSgdUsFt9aAdN6ANoCEdArZbo99tuUHV9lChoBkfANIJmh/RVqGgHTegDaAhHQK2l6XhOxjd1fZQoaAZHQJAATWYnfEZoB03oA2gIR0CtpemXw9aEdX2UKGgGR0CN6ISW7e2vaAdN6ANoCEdAraXpzRx95XV9lChoBkdAkTB3X7Lt/mgHTegDaAhHQK2l6cOskpt1fZQoaAZHQIzQCm0mdAhoB03oA2gIR0CttJNwaR6odX2UKGgGR0CRrnJJXhfjaAdN6ANoCEdArbST655JLHV9lChoBkdAkQ7ASOBDomgHTegDaAhHQK20lOX3QD51fZQoaAZHQJEQtcqvvBtoB03oA2gIR0CttJV89fTkdX2UKGgGR0CRi0QAMlTnaAdN6ANoCEdArcPjafzz3HV9lChoBkdAkiLdthuwYGgHTegDaAhHQK3D46U7jkx1fZQoaAZHQJFO359E1EVoB03oA2gIR0Ctw+PyTY/WdX2UKGgGR0CTWTYXwb2laAdN6ANoCEdArcPkAvL5h3V9lChoBkdAgo+Riw0O3GgHTegDaAhHQK3TJU1AJLN1fZQoaAZHQJFE4IHC4z9oB03oA2gIR0Ct0yWGh24edX2UKGgGR0CRO8ZnctXgaAdN6ANoCEdArdMl0A93bHV9lChoBkdAjvZVGCqZMWgHTegDaAhHQK3TJdUKiPB1fZQoaAZHQJDpTmlqJuVoB03oA2gIR0Ct4djD8+A3dX2UKGgGR0CRO7AcT8HfaAdN6ANoCEdAreHYs3AEdXV9lChoBkdAkVHuWjXWfGgHTegDaAhHQK3h2M3qAz51fZQoaAZHQJHssHX2/SJoB03oA2gIR0Ct4diZv1lHdX2UKGgGR0CIUao3Jgb7aAdN6ANoCEdArfEYL/jsEHV9lChoBkdAiVtQeFL39WgHTegDaAhHQK3xGBzV+Zx1fZQoaAZHQI3ArZOBUaRoB03oA2gIR0Ct8RhRQ79ydX2UKGgGR0CLL1GFSKm9aAdN6ANoCEdArfEYIyCWeHV9lChoBkdAgnCSWZ7Xx2gHTegDaAhHQK3/y8OkLx91fZQoaAZHQHUcHxvvSc9oB03oA2gIR0Ct/8vuPV/ddX2UKGgGR0B8JwvugHu7aAdN6ANoCEdArf/L+aScLHV9lChoBkdAg89W/rSmZWgHTegDaAhHQK3/y8OCoTB1fZQoaAZHQJBUknVoYeloB03oA2gIR0CuDxfQ0GeMdX2UKGgGR0B6lTafzz3AaAdN6ANoCEdArg8Yh0QsgHV9lChoBkdAkFq09ZA6dWgHTegDaAhHQK4PGVk+X7d1fZQoaAZHQJGNom4RVZNoB03oA2gIR0CuDxnTRYzSdX2UKGgGR0CI6WrvLHMmaAdN6ANoCEdArh6zSiM5wXV9lChoBkdAj6ru32EkB2gHTegDaAhHQK4esziS7oV1fZQoaAZHQI6Ic8xKxs5oB03oA2gIR0CuHrNA1NxmdX2UKGgGR0CP5Kg00m+kaAdN6ANoCEdArh6y/KyOaXV9lChoBkdAjaSlLWZqmGgHTegDaAhHQK4tsAWi1zB1fZQoaAZHQIj0v8GcFyJoB03oA2gIR0CuLbAGjbi7dX2UKGgGR0CMVcd4FA3UaAdN6ANoCEdAri2wREnb7HV9lChoBkdAidR2tU4rBmgHTegDaAhHQK4tsA/9pAV1fZQoaAZHQJHzNxBE8aJoB03oA2gIR0CuPOcHfMwDdX2UKGgGR0CRhgyTINmUaAdN6ANoCEdArjzm85CF9XV9lChoBkdAkaBMuvllsmgHTegDaAhHQK485vUBnzx1fZQoaAZHQJHaUUAT7EZoB03oA2gIR0CuPOayB06pdX2UKGgGR0CSqFXCCSRsaAdN6ANoCEdArkwoAZKnN3V9lChoBkdAkobYoVmBfGgHTegDaAhHQK5MKG5+Ytx1fZQoaAZHQJBHEw482aVoB03oA2gIR0CuTClP8AJcdX2UKGgGR0CEs/pRoAXEaAdN6ANoCEdArkwqLjxTbXV9lChoBkdAg/U101ZTymgHTegDaAhHQK5blSkTHsF1fZQoaAZHQJINn/LkjopoB03oA2gIR0CuW5Wy9mHydX2UKGgGR0CRH2E+PikwaAdN6ANoCEdArluWShakh3V9lChoBkdAgS8QJokAxWgHTegDaAhHQK5blpXZGrl1fZQoaAZHQIJ9O3vx6OZoB03oA2gIR0CuayGLcbiqdX2UKGgGR0CPjD9DQZ4waAdN6ANoCEdArmshy2hIv3V9lChoBkdAkHHaMNtqH2gHTegDaAhHQK5rImXPZ7J1fZQoaAZHQIdSlJJ5E+hoB03oA2gIR0CuayJJwsGxdX2UKGgGR0CSQFFyq+8HaAdN6ANoCEdArnpcC/47BHV9lChoBkdAlaLXQyAQQWgHTegDaAhHQK56XAgxJul1fZQoaAZHQJBpHVTaTOhoB03oA2gIR0CuelwS8J2MdX2UKGgGR0CRScPZ7HAAaAdN6ANoCEdArnpb0L+glHVlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.98, "gae_lambda": 0.89, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.15.57.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 #1 SMP Wed Jul 27 02:20:31 UTC 2022", "Python": "3.9.13", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.1", "GPU Enabled": "True", "Numpy": "1.23.1", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:318e2851c433a9206bca02cadc408cbb292afe09b8e5928aa44165bdce20d696
+size 1062189

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1281.1016682639486, "std_reward": 84.04714963142287, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-09-15T14:23:22.399149"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:312c5fe2a86a6cf267e18e27b7e5430201a71477f35ccfb016b8185e291d0ecb
+size 2417