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: 2308.47 +/- 95.04
+      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 sha256:7ed204f5afab3a4abf84b2fe489a9f87417400f9040f44188ac8da3f3075547b
+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 0x7f215df5fc10>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f215df5fca0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f215df5fd30>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f215df5fdc0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f215df5fe50>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f215df5fee0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f215df5ff70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f215df64040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f215df640d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f215df64160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f215df641f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f215df64280>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f215df5c8d0>"
+    },
+    "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": 1674050606302027069,
+    "learning_rate": 0.00096,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAHAwpT80wa0+NeJ5PnYLwT/2BEY/BZcYv6a6Nz54QR+/UtY9vxE/x74Gw1m+n3sbviQ6jT86eYG/YCNmPtU5GUCXU96+NutdvxHSkL7BTYq/MI+Xvx74Cj9RT5w+dhu/vv3IPL+hjMK/wG6pPl09rr/vcaE/DyGUPpXBlz4KfKI/sW+PPx3Eo7//GW89ig1jvxRST70UYIo+7Awqv++kjb5yBFY/brRBv8AFBj/5qUK+tf4/v42LQb7JQ/+9lJCZv2CadL+W0sg/mPU3P1iyMD79yDy/oYzCv8BuqT5dPa6/PLVaP49bPr/ZvGI/KwaOvlsxrr4XzS4+tnyVv/wISD/+ZGM/K/EAQI2pDD8lLZ8/w2q+v3VZIT+9/me/kTB6vvYS+j+SyR+/qblFwCjM+D5m5JM/xaElv2fT9r51udI/sJKtPyZuKD/Abqk+XT2uv2ldrD8LMJs+q4OQPqiBvD9/3ms/1/J7vPcELT8WTDK/jw+Yv5YIaru7aj691IzWPWXDoD8sMNy+jla7vg9iKECnMIa/1oBjPj3Su72tznW/96Sjv2RAJD81b+o8clumPf3IPL8mbig/wG6pPhAQPD+UjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"
+    },
+    "_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:": "gAWVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQJysC86FM7GMAWyUTegDjAF0lEdAqkpximVJMHV9lChoBkdAnC/gAp8WsWgHTegDaAhHQKpLixagVXV1fZQoaAZHQJ3KAg7o0Q9oB03oA2gIR0CqTV5LytmudX2UKGgGR0CdkqEaESM+aAdN6ANoCEdAqk8nv6TGHnV9lChoBkdAoOThmseXA2gHTegDaAhHQKpWlJ6po9N1fZQoaAZHQJ01G+BYmsxoB03oA2gIR0CqV64zi0fHdX2UKGgGR0ChKce6I3zdaAdN6ANoCEdAqlmLronrp3V9lChoBkdAoMseOjqOcWgHTegDaAhHQKpbQ3R5TqB1fZQoaAZHQKBMzA+IM0BoB03oA2gIR0CqYo+uV5bAdX2UKGgGR0CgltM4T9KmaAdN6ANoCEdAqmOvgzguRXV9lChoBkdAoNvcZ75VO2gHTegDaAhHQKpli9rXUYt1fZQoaAZHQKAMRuUliSdoB03oA2gIR0CqZ2IAOrhjdX2UKGgGR0ChF88U21lYaAdN6ANoCEdAqm618b70nXV9lChoBkdAoUv7L4etCGgHTegDaAhHQKpvypx3mmt1fZQoaAZHQKA37ThHbypoB03oA2gIR0CqcaDafzz3dX2UKGgGR0ChEafzJ6ppaAdN6ANoCEdAqnNjE9+w1XV9lChoBkdAn7qwTqSowWgHTegDaAhHQKp6zeb/ffp1fZQoaAZHQKA3kVzIV/NoB03oA2gIR0Cqe/hMajvedX2UKGgGR0Cd25zfJmulaAdN6ANoCEdAqn3ymGdqcnV9lChoBkdAoHszfcer/GgHTegDaAhHQKp/t4+KTB91fZQoaAZHQJfBrarWAgBoB03oA2gIR0CqhwJxWDHwdX2UKGgGR0CetGsDGLk0aAdN6ANoCEdAqoghcVxjrnV9lChoBkdAmC9R11W8y2gHTegDaAhHQKqKAKYzBRB1fZQoaAZHQJyjvViF0xNoB03oA2gIR0Cqi9O5avA5dX2UKGgGR0CcNSGN70FsaAdN6ANoCEdAqpMnuE25x3V9lChoBkdAoETVNrTH82gHTegDaAhHQKqURX7Lt/p1fZQoaAZHQKAxLCiyprFoB03oA2gIR0CqliH1e0HAdX2UKGgGR0CgdPZflZHNaAdN6ANoCEdAqpfnSH/LknV9lChoBkdAn/6r0e2d/mgHTegDaAhHQKqfV0KZ2IR1fZQoaAZHQJ71HcDbJwNoB03oA2gIR0CqoHIna37UdX2UKGgGR0CdSyYSg5BDaAdN6ANoCEdAqqJJbY9PlHV9lChoBkdAnrRhi5NGmWgHTegDaAhHQKqkBQrMC911fZQoaAZHQJ/15XA/LTxoB03oA2gIR0Cqq4jEWIoFdX2UKGgGR0CggQZ6Uqx1aAdN6ANoCEdAqqymiBXjl3V9lChoBkdAoIpIv6CUYGgHTegDaAhHQKqubZEDyOJ1fZQoaAZHQKDn/l/YraxoB03oA2gIR0CqsDZ2pyZKdX2UKGgGR0CgWS9hAnlXaAdN6ANoCEdAqriSJXQtz3V9lChoBkdAnwPmSIP9UGgHTegDaAhHQKq5vURWcSZ1fZQoaAZHQJ+RxNnGsFNoB03oA2gIR0Cqu5uhbnoxdX2UKGgGR0Cgj6pr1uiwaAdN6ANoCEdAqr1qTdLxqnV9lChoBkdAnMVWRaHKwWgHTegDaAhHQKrE2zoEB8x1fZQoaAZHQJ5WYeo1k2BoB03oA2gIR0CqxgU1AJLNdX2UKGgGR0Ccl417Y02taAdN6ANoCEdAqsfWtEG7jHV9lChoBkdAniBJudf9gmgHTegDaAhHQKrJnfNRm9R1fZQoaAZHQJ7oXTEzfrNoB03oA2gIR0Cq0S+Vkc0cdX2UKGgGR0CgVJtzbN8maAdN6ANoCEdAqtJHLDAJs3V9lChoBkdAoD5qnJkoW2gHTegDaAhHQKrUHDIBBAx1fZQoaAZHQKFa++X7cfxoB03oA2gIR0Cq1eGHP/rCdX2UKGgGR0Cfzer/sE7oaAdN6ANoCEdAqt18LjPv8nV9lChoBkdAoNUO4kNWl2gHTegDaAhHQKremG5+Ytx1fZQoaAZHQKBS9SmZVn5oB03oA2gIR0Cq4HZHmRvFdX2UKGgGR0ChJIOHvc8DaAdN6ANoCEdAquI9XRw6yXV9lChoBkdAne/OpGWldmgHTegDaAhHQKrp2GGEf1Z1fZQoaAZHQJ8gnHFPznRoB03oA2gIR0Cq6wKEOAiFdX2UKGgGR0Ce/qXjU/fPaAdN6ANoCEdAquzb+vQnhXV9lChoBkdAn+mgjY7JXGgHTegDaAhHQKruq5q/M4d1fZQoaAZHQKAPm9Net0VoB03oA2gIR0Cq9iRWT5fudX2UKGgGR0CfYy6Vt4zKaAdN6ANoCEdAqvdG/L1VYXV9lChoBkdAnpJJ5E+gUWgHTegDaAhHQKr5Nin5zo51fZQoaAZHQKBNAfdyksVoB03oA2gIR0Cq+v92ovSMdX2UKGgGR0CcxoLdepn6aAdN6ANoCEdAqwKnvv0AcXV9lChoBkdAoEiktK7I1mgHTegDaAhHQKsDwhLXcxl1fZQoaAZHQJ5cEoQWepZoB03oA2gIR0CrBaV6Vt4zdX2UKGgGR0Ccqs3VkMCtaAdN6ANoCEdAqwd45WBBiXV9lChoBkdAnnaTDwYtQWgHTegDaAhHQKsO2371qWV1fZQoaAZHQJoRm4H5aeRoB03oA2gIR0CrD/UKJEYwdX2UKGgGR0CfC1E5QxetaAdN6ANoCEdAqxHPhsImgXV9lChoBkdAoB0hN7BwdmgHTegDaAhHQKsToQjD8+B1fZQoaAZHQJ/vTKW9lEtoB03oA2gIR0CrGxJfhMrVdX2UKGgGR0ChcQqubI91aAdN6ANoCEdAqxwn6XSjQHV9lChoBkdAnnp+iBXjl2gHTegDaAhHQKseDJQtSQ51fZQoaAZHQKCK1y9VWCFoB03oA2gIR0CrH9Pa+N96dX2UKGgGR0CgsBBGH58CaAdN6ANoCEdAqydBo/Rmb3V9lChoBkdAnzFIQarFO2gHTegDaAhHQKsobedCmdl1fZQoaAZHQKCjYK1og3doB03oA2gIR0CrKk81O0swdX2UKGgGR0ChhbvXCj1xaAdN6ANoCEdAqyxjDMvAXXV9lChoBkdAoTjSOJcgQ2gHTegDaAhHQKs0Usrd30R1fZQoaAZHQKBgNK4hEBtoB03oA2gIR0CrNXYpc5bRdX2UKGgGR0CgnP7fxc3VaAdN6ANoCEdAqzdgI0IkaHV9lChoBkdAn6BZIQOFxmgHTegDaAhHQKs5MJvYODt1fZQoaAZHQJ+FlXFLnLdoB03oA2gIR0CrQKly7wrldX2UKGgGR0CdmXrTpgTiaAdN6ANoCEdAq0HKXMQmNXV9lChoBkdAmUtxh+fAbmgHTegDaAhHQKtDo6NEPUd1fZQoaAZHQKDQdekYXO5oB03oA2gIR0CrRWi1qnFYdX2UKGgGR0CdwuND+irUaAdN6ANoCEdAq0zhG8VYZHV9lChoBkdAnXgzUZvUBmgHTegDaAhHQKtN+S7oSth1fZQoaAZHQJyAvi0fHPxoB03oA2gIR0CrT9i1AqusdX2UKGgGR0CgC40t7KJVaAdN6ANoCEdAq1GZa9sabXV9lChoBkdAoJW6oddVvWgHTegDaAhHQKtZCrH2h7F1fZQoaAZHQJoxMN6PbPBoB03oA2gIR0CrWjBO58SgdX2UKGgGR0CgzWnrQgLaaAdN6ANoCEdAq1wIn2Iwd3V9lChoBkdAoIv8tXgccWgHTegDaAhHQKtdzyVfNRp1fZQoaAZHQKGEa2a2F39oB03oA2gIR0CrZUmnn+yadX2UKGgGR0CiYgYukDZEaAdN6ANoCEdAq2Zil54W13V9lChoBkdAogY7pgTh52gHTegDaAhHQKtoP71qWTp1fZQoaAZHQKHP+71ZkkNoB03oA2gIR0Craf+Ad4mkdX2UKGgGR0Ciog7W/ag3aAdN6ANoCEdAq3F9utOmBXV9lChoBkdAoh7srI5o5GgHTegDaAhHQKtyljjrAxl1fZQoaAZHQKJw9Y3eenRoB03oA2gIR0CrdHMTN+spdX2UKGgGR0CiIC+i8FpxaAdN6ANoCEdAq3ZFMqSX+nVlLg=="
+    },
+    "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": 0.4,
+    "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:db8693a8ccd7d6370b7da32db755f527049dcf9e061586415f9bb7ad3d66d6b8
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bb4833bda0ebb71c9545432b145d8ea8198aef3f1994e7b7f4df16087d08ac36
+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: 1.21.6
+- 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 0x7f215df5fc10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f215df5fca0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f215df5fd30>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f215df5fdc0>", "_build": "<function ActorCriticPolicy._build at 0x7f215df5fe50>", "forward": "<function ActorCriticPolicy.forward at 0x7f215df5fee0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f215df5ff70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f215df64040>", "_predict": "<function ActorCriticPolicy._predict at 0x7f215df640d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f215df64160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f215df641f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f215df64280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f215df5c8d0>"}, "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": 1674050606302027069, "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": 0.4, "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": "1.21.6", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d667bfda6fd84fb3de14d8ac69cfc22e60eb76216bee522a44a8da4590fc2d81
+size 1327857

results.json

@@ -0,0 +1 @@
+{"mean_reward": 2308.468536232528, "std_reward": 95.03637510086054, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-18T15:01:23.958599"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:666f0ffcbffd655b1b0533b5a47e0b8e5012a2cb7ee06a4c35c958ca025e7480
+size 2521