Initial commit

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: 774.36 +/- 110.18
+      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 sha256:460a35137fb204ded10aa24dc356c2068bc7ceb1174916bcdb5bf112939195b3
+size 132594

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,107 @@
+{
+    "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 0x7c08e8e5d000>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c08e8e5d090>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c08e8e5d120>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c08e8e5d1b0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7c08e8e5d240>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7c08e8e5d2d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c08e8e5d360>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c08e8e5d3f0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7c08e8e5d480>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c08e8e5d510>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c08e8e5d5a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c08e8e5d630>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7c08e8e55a40>"
+    },
+    "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
+        }
+    },
+    "num_timesteps": 200000,
+    "_total_timesteps": 200000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1690552387346093352,
+    "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,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 6250,
+    "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,
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWV9AwAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLHIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWcAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/lGgLSxyFlIwBQ5R0lFKUjARoaWdolGgTKJZwAAAAAAAAAAAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH+UaAtLHIWUaBZ0lFKUjA1ib3VuZGVkX2JlbG93lGgTKJYcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLHIWUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUaCJLHIWUaBZ0lFKUjApfbnBfcmFuZG9tlIwUbnVtcHkucmFuZG9tLl9waWNrbGWUjBJfX3JhbmRvbXN0YXRlX2N0b3KUk5SMB01UMTk5MzeUhZRSlH2UKIwNYml0X2dlbmVyYXRvcpRoMYwFc3RhdGWUfZQojANrZXmUaBMolsAJAAAAAAAA1NTzv6qw7wSEMR5fOCSExh43rD4z1gj3vJfcpOqso6GO7gyaInP8jDkUyQFlPSSY8H8+HtjaKVRLbSKOdb4/ZpvKx5dkmVB2xp0BDe29T5MbTmRYVC0Ae4qwVGbP5SlzGfvQ2AsdusYigffPPNiJWN2fLORaULHuqscWghKjUP9n+9Se2Z26JSl9pTnOCUu0327X7dBMuXppzyJWp9PRtBDYkfE6jemy+2A4Mi/P3+GdV/FdTJzzagZ0NQ6X2T/t7eMGyUvNG235/1+kpaMyV7GsFbwPEf8Q1i8hfzmN4IyMODj02rwjJarvf3eyYepqZEJgl+5ytIlwpor07PIbMdWkCFe9NLrCWXz2tgXOcgofpiigAUhERN5FCe5wKnDIfk24Q0yJye2VYzl6Mc+l4Uofx5/15RI4ZRnL0VxuDh32iz7UCvsLF3zE+dhEo202YYsL33WizjbjAsTWA4MsHFXnFb82f2R0rLPa6AeQAl8nGZdm+sUGukY4zLweEOVB/KU7KqYLJ1iwNJj6gMM9E8HYyXdFtvFkeJbwRXnCgZvQ+wUp2YCGnThondNKmtCGJWA0Cf8yvaGd01jkki7zO5buVpiiuCyN4AK7OCmqDeFXXfpG881toT/jCfTxRbzPwUq/Z7J8Sxe20hy8JAAQVPL9yOrNVXE9Vf5VwbZn8W9aNQ6WN45LPoEcdZYIm/iT5wWyQHMf/QSzR5lEVl9kfb7wvKFcXb5EMjojAm7t5FHW68PQLlXLLi2yNMcNRG4GrJytTJtECJsUFAXLOJXHQhsqya2hW3t5RUPnRj7TAFDdvf+GlyKrqzQU3ceiwOLJOWO7RzPZyesIXwF9SIPzO3NQpCoeqBcr9wFGiKr2r4TmucDDLmQoz2MeyL85xeapWIH8sVwElHWh/rR4s35Hd/1XedP02DTyVW2Y7G7Sw8l40Mwiglcx1ax6CB8owNufCloT/BqJwDrZne3IMOGICdSsof9RcQMrlzFgWB1ri0qpDeLVM5VCT0KspCh57Kg7tHgDGSFbecIhbIWiDKuW7Z4dkdJZmxmZphOztI402YDAEsCvzb3hFiuwVcAdwF1ckJHCd+ASRJsuEqScWzuK3p0TV0FZciyC7wiKij6BdUsT2Cnq9x6iIHuDw9xNlcgG5GZY2b6GoUtG9RtJVHGztqswOZZp/FZMnwBKqJyXAMnotCigMObMOY/gNJKhH0G2PdYMzK3VGle0loprGF8awlE7XAgy0Yg/2LJMkQrP+OWGfh+Mdved/Adc5V/3YgwW0QSJf9eXB0k6B4rK2qSB7FPPHSShPfHP3BhpuvvYtijLMa+ARdzMck0eaLEu4g9L8Dyz1w+0kmR7aXdf8bqJRRoaC44/0762fa9m/zmUJmBlso9XK1f1CnHLiGaJ2XFyQ0LWlG94JrGBXSs2vnoGfjFnnhJpsVGyE31gfSlnJ6dS1H8/jTkXK16Q0iyPGMSpG/xOj+7pponU4TGk8tb/cpvDVHi4Dt31khcnOyZPr3FyT2DcYJEDaPbFRWTvyjpdH0aWmf5d0JJT+1xxt5tuXifD7NlXnL6walTIzohCyKg1IbuqVYpNniFvBL8Cr6ks/RbiA1wfw+9FQN/ik9nGosi14tv0mtWqczFt6xY/QrlQ29D1XPLFJZmGeZCdTsoupDh0lRhaTy+mF0tVO2EazkH3IKIB5QV/HJCxatL5KdhSNyMdOsk2CUvDWZBb0YhgkKcQRLCfKnZfNsLROQnB3RlAwSfj6hUHb3V56x9AFa51r2G4OGUc15t6JNf+v2+rAuKKAlv425GnkOOSSID+kMTYTxH6E5pQraHejjtGmIpSqzGhDt8o+9yLMBwc25/5DM1os7uQHcwf071dUwVCU6SyMOUvhxFPZ/cTve98wyXFpBAb5S5DQrBFiXGtq+24J8NWoukPh/+dFNEmVzCkDZmohp01RjfncZnqiU8cd8NGbrSBKLkb73m4dqA6LfVmGps49WUOR+AiyKnevvjwulsjZb/wCV1Nlv4vjnia9B/cM7mhep/zddZXwHbzMfc+f1SqTUat55m315/jGTBKETKndBVzkXq5iq9hnma/akMXXtOHFHsvkoAUjHsKhZb4dixXX1sxRc+0xGJIRRIzGoh0a1zDmOsvVXyedJO04j5ToSdv6oHfBs/QbEnf6ZbHMfNYD1RMB2ZKgIWxKiqqp7dVXuG6v8/aJPWV2jOC6r4O5XWTD57EojBl3TgzkR78tHK0PIg8yj0+0prGqQDOoEq34tJxMgF6qPbaG+hA9qMnjxcZ4Jw+tHxOFcqpZd1zPkoV6ewJK47f/WxkQMM1Quvezqc9mY4U52IuiV9ahw0+G8jh22mdZObNWHk3Vo+/cf910xHckc7JrzR3Msy4usWP2JeEzMr0dx7te8R+qh7iKSgCJejX3dsX/5lqGs9fBwEQKqA5jxXvRxUwwLDZXQ+ZUghs9lzXcCQlhXegYLy4gX4L5fdfRYmFfbKzAe7jVlaPDfgmdNP8jGE5vKSaBiMC2stHJRhCozx3RpDk73ycMdziHsr1Qmsg5Vv5YfLDPPQqFS8oSOs0h+X8NBdrV4dO/cuW5kvS6Ns2Ko9kt09/aBKbmzJ0TduiElQnAgD921jp/1aJ+OXqB4avh/+EctOTysqo/TAVJ9DEM+vhN0FP5d1hiLsClUQJ6U24B/RfqVn5uBfleCLtgWPDum5YqHiBMtFmp4pCe2JRI5d1w7QXUth8Mpks3iSAqTJ9drZcKjg4hguIFa6Wwi1ro4DiOSg4F7eCulmtK4GFFvZ9WLrx9gBan8xKhvjipRvX6flk9hvrSloM37jNM29TjgkDwJ5Ynaqof7XA7Iuw39r9lzgkXz3kAXbXG8n/MhJhVOCQNTUsgkyAfK6GVeF1QCMBnF1e8KnthsOYrH3bKGsT5GhUaPymUNiFWHjIQKnb9Z7gtPnmVMXrmbiEh50XHgTh5stFDtiM+2RSb3ODhcqiBQvWXeclhQIfsePwbMRzXHwKkezhCmQxhit6/A2UzUM/suId6vNhfLXe75pwCcy+jZtUWkR4mWqwrdCCgTfENJTMi9wuCzX/SgXyz7STZnHSp6j5jmxbk8OpQ4L/GU3oCEUM9bSHG8GYlz5TrokZHJAMmGApYDFXNkumXQVvdD6s9KvYeV4x0NiL8JmShkiOU9cnts9K/vTccsoza3yF+BbTH7LnO9R9gUiiM+hGiY0IWlKSVzc0mtsfW/pKrtr1g/d4OBMY1J5c069WcG20GE2hEKmsjlMhHBpTu71JPIgdVFXxNgGpIdIqRBtEJOTNwPABsqWnxjfdhOqU/t0cxq5AlGgIjAJ1NJSJiIeUUpQoSwNoDE5OTkr/////Sv////9LAHSUYk1wAoWUaBZ0lFKUjANwb3OUS0x1jAloYXNfZ2F1c3OUSwCMBWdhdXNzlEcAAAAAAAAAAHVidWIu",
+        "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": "RandomState(MT19937)"
+    },
+    "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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0f1c994b9e252b30f349335d7a73386a34ce140e8f8ca32269fe3b481fec25aa
+size 56062

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:371e96bc287534627813d89861b40eaafafee81fa34a1c4d69491fd68c178197
+size 56766

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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.6
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: False
+- 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 0x7c08e8e5d000>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c08e8e5d090>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c08e8e5d120>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c08e8e5d1b0>", "_build": "<function ActorCriticPolicy._build at 0x7c08e8e5d240>", "forward": "<function ActorCriticPolicy.forward at 0x7c08e8e5d2d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c08e8e5d360>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c08e8e5d3f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7c08e8e5d480>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c08e8e5d510>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c08e8e5d5a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c08e8e5d630>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7c08e8e55a40>"}, "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}}, "num_timesteps": 200000, "_total_timesteps": 200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690552387346093352, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9PdRBNVR1phZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "_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, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 6250, "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, "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": "RandomState(MT19937)"}, "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, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "False", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 774.3608664668384, "std_reward": 110.1837112917646, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-28T14:04:34.981946"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07c5f358e93d330e297ce2d214c1667b359604087b9979f8cb17b393ae8fecb5
+size 4879