Initial commit

.gitattributes

@@ -32,3 +32,4 @@ 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 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- AntBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: AntBulletEnv-v0
+      type: AntBulletEnv-v0
+    metrics:
+    - type: mean_reward
+      value: 1156.06 +/- 321.36
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **AntBulletEnv-v0**
+This is a trained model of a **A2C** agent playing **AntBulletEnv-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-AntBulletEnv-v0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b8ec61494c1450c72b748fbf5aeba8949cbf6808e8017e53b5713c1c6f10502
+size 129260

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,106 @@
+{
+    "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 0x7fdf98332310>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdf983323a0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdf98332430>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdf983324c0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fdf98332550>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fdf983325e0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fdf98332670>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdf98332700>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fdf98332790>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdf98332820>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdf983328b0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdf98332940>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fdf98325d20>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
+        "log_std_init": -2,
+        "ortho_init": false,
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            28
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
+        "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1. 1. 1. 1. 1.]",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1677942611508083161,
+    "learning_rate": 0.00096,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_episode_num": 0,
+    "use_sde": true,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQJEp/7bcoH+MAWyUTegDjAF0lEdAtHq4SXdCV3V9lChoBkdAh57ZsKsuF2gHTegDaAhHQLR7YYxL0z11fZQoaAZHQI31L0Fr2xpoB03oA2gIR0C0e30/KQq7dX2UKGgGR0CQ0Q3VCojwaAdN6ANoCEdAtHuEbFS88XV9lChoBkdAlJDQJgLJCGgHTegDaAhHQLSDUi3ocJd1fZQoaAZHQIl9xCOWBz5oB03oA2gIR0C0hFQ5imVJdX2UKGgGR0CR/+NZ/0/XaAdN6ANoCEdAtIR5vWH1vnV9lChoBkdAk5PmBWgezWgHTegDaAhHQLSEhsP8Q7N1fZQoaAZHQJBce4kNWlxoB03oA2gIR0C0jBcWj45+dX2UKGgGR0B85ltHhCMQaAdN6ANoCEdAtIy8T+NtInV9lChoBkdAjg6yrxRVImgHTegDaAhHQLSM0xeLNwB1fZQoaAZHQJANsD0UXYVoB03oA2gIR0C0jNsDB/I9dX2UKGgGR0CSVmxkNFz/aAdN6ANoCEdAtJXcPatcOnV9lChoBkdAkqVd6gM+eWgHTegDaAhHQLSWhyZa3Zx1fZQoaAZHQJKysWRA8jloB03oA2gIR0C0lqDdcjZ+dX2UKGgGR0CLR7IXCTEBaAdN6ANoCEdAtJaoLx7RfHV9lChoBkdAlwT7lJYkmmgHTegDaAhHQLSdunhKlHl1fZQoaAZHQI4ZRgssg+1oB03oA2gIR0C0nmRMewLWdX2UKGgGR0CTOq0JF9a2aAdN6ANoCEdAtJ5+fTTfBXV9lChoBkdAhueeSjgydmgHTegDaAhHQLSehaAFxGV1fZQoaAZHQIVsSRU3n6loB03oA2gIR0C0p7CWNWELdX2UKGgGR0CPmyr8R+SbaAdN6ANoCEdAtKiqJIlMRHV9lChoBkdAh+xsyi22HGgHTegDaAhHQLSo0awD/2l1fZQoaAZHQIKd7TpgTh5oB03oA2gIR0C0qNyuU2UCdX2UKGgGR0CI5shew9q2aAdN6ANoCEdAtLKvPomoi3V9lChoBkdAkKn212JSBWgHTegDaAhHQLSzxV2Rq491fZQoaAZHQIzwmvbGm1poB03oA2gIR0C0s+yg00m/dX2UKGgGR0CPZnDZUT+OaAdN6ANoCEdAtLP4MDwH7nV9lChoBkdAjitBwl0HQmgHTegDaAhHQLS7VTH80k51fZQoaAZHQHojyeI2wV1oB03oA2gIR0C0vAjvAoG6dX2UKGgGR0CEsZ6TGHYZaAdN6ANoCEdAtLwg45tFa3V9lChoBkdAiyox9gF5fWgHTegDaAhHQLS8KDAaef91fZQoaAZHQIl8EE3bVSZoB03oA2gIR0C0xUCwGGEgdX2UKGgGR0CMZ+tHQQcxaAdN6ANoCEdAtMX28DjioHV9lChoBkdAjNKsspXp4mgHTegDaAhHQLTGE4lyBCl1fZQoaAZHQIjp2fVZs9BoB03oA2gIR0C0xhs5n13/dX2UKGgGR0CNI+YEW69TaAdN6ANoCEdAtM0yf7Jnx3V9lChoBkdAiuPx//echGgHTegDaAhHQLTOCEsasIV1fZQoaAZHQIq8toexOcloB03oA2gIR0C0zixREWqMdX2UKGgGR0CIGTgYxcmjaAdN6ANoCEdAtM427ROUMXV9lChoBkdAki5gBkqc3GgHTegDaAhHQLTW90AcT8J1fZQoaAZHQJAnwYcebNNoB03oA2gIR0C015+dsi0OdX2UKGgGR0CQSOtdAxBWaAdN6ANoCEdAtNe8xpL26HV9lChoBkdAgF6LNW2gF2gHTegDaAhHQLTXw+w1R+B1fZQoaAZHQIqqtqrR0EJoB03oA2gIR0C036hxcVxkdX2UKGgGR0CLbAIomXw9aAdN6ANoCEdAtOCwZP2wmnV9lChoBkdAhuVEK/mDDmgHTegDaAhHQLTg2GsFMZh1fZQoaAZHQItbCRQrMC9oB03oA2gIR0C04OToyKvWdX2UKGgGR0CRYPl/6O5saAdN6ANoCEdAtOiMf0VafXV9lChoBkdAip2CEYfnwGgHTegDaAhHQLTpNd07r9l1fZQoaAZHQJDMEsasIVxoB03oA2gIR0C06U6aPS2IdX2UKGgGR0CN+pzjm0VraAdN6ANoCEdAtOlVdqtYCHV9lChoBkdAk62i0a6z3WgHTegDaAhHQLTyK0m+j/N1fZQoaAZHQJQNfAN5MURoB03oA2gIR0C08te1jRUndX2UKGgGR0CUKcS2phnbaAdN6ANoCEdAtPLvmwJPZnV9lChoBkdAk0/UGqxTsWgHTegDaAhHQLTy9pmVZ9x1fZQoaAZHQJEB3QzDXOJoB03oA2gIR0C0+fJNCZ4OdX2UKGgGR0CRfu01IiC8aAdN6ANoCEdAtPqYy8BdU3V9lChoBkdAk7MDujRD1GgHTegDaAhHQLT6r8rZrYZ1fZQoaAZHQJGk/5sTFl1oB03oA2gIR0C0+rcpgCwKdX2UKGgGR0CQh5Lk0aZQaAdN6ANoCEdAtQORV4oqkXV9lChoBkdAg6NpcPe54GgHTegDaAhHQLUEPkleF+N1fZQoaAZHQJCt4sJ6Y3NoB03oA2gIR0C1BFccENe/dX2UKGgGR0CTraULUkOaaAdN6ANoCEdAtQRea8YhuHV9lChoBkdAlY+JFXq7iGgHTegDaAhHQLULZzvJA+p1fZQoaAZHQJGy5S88La5oB03oA2gIR0C1DFuEVWS2dX2UKGgGR0CPsDcpsoDxaAdN6ANoCEdAtQyDJq7AcnV9lChoBkdAjoftm+TNdWgHTegDaAhHQLUMjpdrwfB1fZQoaAZHQIjdhHTZxrBoB03oA2gIR0C1FSF9nbqRdX2UKGgGR0CRJ9hjvuw5aAdN6ANoCEdAtRXSN96Tn3V9lChoBkdAlRRxmXgLqmgHTegDaAhHQLUV6eU6gdx1fZQoaAZHQJTWXk1dgOVoB03oA2gIR0C1FfDRMN+cdX2UKGgGR0CRp331zySWaAdN6ANoCEdAtR5M99tuUHV9lChoBkdAj9TtknTiKmgHTegDaAhHQLUfXE5hjON1fZQoaAZHQI+rKGSIP9VoB03oA2gIR0C1H4V/H5rQdX2UKGgGR0B6gxuk1uR+aAdN6ANoCEdAtR+RN8E3bXV9lChoBkdAjBqQEpy6tmgHTegDaAhHQLUmx4NZvDR1fZQoaAZHQJRYHKzRhMJoB03oA2gIR0C1J3JLRKHxdX2UKGgGR0CTxVfek56uaAdN6ANoCEdAtSeLYXfqHHV9lChoBkdAkm7QgcLjP2gHTegDaAhHQLUnlTJyQxN1fZQoaAZHQJgDo3kxREZoB03oA2gIR0C1MHeCPIXCdX2UKGgGR0B3Uc8dPtUoaAdN6ANoCEdAtTEnEuQIU3V9lChoBkdAk/qqRZEDyWgHTegDaAhHQLUxQBJI1+B1fZQoaAZHQIgN0y+HrQhoB03oA2gIR0C1MUcrAgxKdX2UKGgGR0COxjPAwfyPaAdN6ANoCEdAtThhwZOzp3V9lChoBkdAkWEMBdUsF2gHTegDaAhHQLU5FpT/ACZ1fZQoaAZHQI/tbDwYtQNoB03oA2gIR0C1OS45tFa0dX2UKGgGR0CTIac8kleGaAdN6ANoCEdAtTk2dEsrd3V9lChoBkdAk13LR0EHMWgHTegDaAhHQLVB7schkiF1fZQoaAZHQI4f9GZuyeJoB03oA2gIR0C1Qph68g6mdX2UKGgGR0CT8fZvUBn0aAdN6ANoCEdAtUKxUT+NtXV9lChoBkdAlLkagVXV9WgHTegDaAhHQLVCuJzT4L11fZQoaAZHQI/NiP+4smRoB03oA2gIR0C1SlfAoG6gdX2UKGgGR0CY+wBDG96DaAdN6ANoCEdAtUtiN4qwyXV9lChoBkdAlxdAJLM9sGgHTegDaAhHQLVLiGVRk3F1fZQoaAZHQJMaNPnB+F1oB03oA2gIR0C1S5R3qzJIdX2UKGgGR0CWEHYx+KCQaAdN6ANoCEdAtVNIEnssx3V9lChoBkdAly+rNW2gF2gHTegDaAhHQLVT9m9g4Ot1fZQoaAZHQJaIT6guh9NoB03oA2gIR0C1VBCdFvycdX2UKGgGR0CU3OAiV0LdaAdN6ANoCEdAtVQYKJEYwnVlLg=="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 62500,
+    "n_steps": 8,
+    "gamma": 0.99,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdd58d5e612f026036336d9afb0a1f703591a941cc7c6ab372cf75a8e78c0f8d
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5faa49499ae56ec4eb4e8a56280845fc05a32081068e6a42db00c879a67ee3fe
+size 56958

a2c-AntBulletEnv-v0/pytorch_variables.pth

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

a2c-AntBulletEnv-v0/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

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 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 0x7fdf98332310>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdf983323a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdf98332430>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdf983324c0>", "_build": "<function ActorCriticPolicy._build at 0x7fdf98332550>", "forward": "<function ActorCriticPolicy.forward at 0x7fdf983325e0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fdf98332670>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdf98332700>", "_predict": "<function ActorCriticPolicy._predict at 0x7fdf98332790>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdf98332820>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdf983328b0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdf98332940>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fdf98325d20>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [28], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677942611508083161, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4384569109d9e658e36fc7c63e92a67c50f192728cd12b5bc9031636d50cee90
+size 1049519

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1156.0627316503494, "std_reward": 321.3633381418895, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-04T16:28:14.052159"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:80143a8d0c55858fcf67c5871366302beb9f0da8f3abbd6b37b570084e366fa7
+size 2136