Eerste 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:
+- HalfCheetahBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: HalfCheetahBulletEnv-v0
+      type: HalfCheetahBulletEnv-v0
+    metrics:
+    - type: mean_reward
+      value: 827.20 +/- 108.15
+      name: mean_reward
+      verified: false
+---
+
+# **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 sha256:2358f7429a8609b01fecd85c88ad3794c74857d1755437abf9976d73a83f346a
+size 124952

a2c-HalfCheetahBulletEnv-v0/_stable_baselines3_version

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

a2c-HalfCheetahBulletEnv-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 0x7f09cc1e3550>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f09cc1e35e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f09cc1e3670>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f09cc1e3700>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f09cc1e3790>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f09cc1e3820>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f09cc1e38b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f09cc1e3940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f09cc1e39d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f09cc1e3a60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f09cc1e3af0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f09cc1e3b80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f09cc1e1150>"
+    },
+    "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": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1674935111349966054,
+    "learning_rate": 0.00096,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/T3UQTVUdaYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_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:": "gAWVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQJBBw1hsqKCMAWyUTegDjAF0lEdAlN+eieumrXV9lChoBkdAkDg/bTMJQmgHTegDaAhHQJTfnzK9wm51fZQoaAZHQI+G8m6XjVBoB03oA2gIR0CU35/oq0+ldX2UKGgGR0CP1bF6Rhc8aAdN6ANoCEdAlN+gjhUBGXV9lChoBkdAkhuhpHqeLGgHTegDaAhHQJT2K6DoQnR1fZQoaAZHQIr5YfdRBNVoB03oA2gIR0CU9ixOtW+5dX2UKGgGR0CPkTPomoitaAdN6ANoCEdAlPYtFa0Qb3V9lChoBkdAjsTg3Lmp2mgHTegDaAhHQJT2LbqQiiZ1fZQoaAZHQI1NigAZKnNoB03oA2gIR0CVDLceKbazdX2UKGgGR0CQViZq20AtaAdN6ANoCEdAlQy3z6JqI3V9lChoBkdAi/ouSntOVWgHTegDaAhHQJUMuOdXko51fZQoaAZHQI5Azyz5XU9oB03oA2gIR0CVDLo0hvBKdX2UKGgGR0CKXYCQtBfKaAdN6ANoCEdAlSNcBltj1HV9lChoBkdAjB6xn3+MqGgHTegDaAhHQJUjXL3bmEJ1fZQoaAZHQI2bJ9y925hoB03oA2gIR0CVI13KB/ZvdX2UKGgGR0CPNYzMzMzNaAdN6ANoCEdAlSNeogmqpHV9lChoBkdAjKT3mFJxvWgHTegDaAhHQJU6IfcN6Pd1fZQoaAZHQIz9bvd/J/5oB03oA2gIR0CVOiLl3hXKdX2UKGgGR0CQCB7/GVAzaAdN6ANoCEdAlToj3VTaTXV9lChoBkdAj8m6CcwxnGgHTegDaAhHQJU6JMlC1JF1fZQoaAZHQI0CinP3SKFoB03oA2gIR0CVUKuFpPAPdX2UKGgGR0CQposGPgejaAdN6ANoCEdAlVCsSPEKmnV9lChoBkdAj05fk3juKGgHTegDaAhHQJVQrPPcBU91fZQoaAZHQI7WE0gr6LxoB03oA2gIR0CVUK2Qnx8VdX2UKGgGR0CQ2q71qWTpaAdN6ANoCEdAlWcY1UEPlXV9lChoBkdAkNShkupS8GgHTegDaAhHQJVnGakRBeJ1fZQoaAZHQJEDoeo1k2BoB03oA2gIR0CVZxqASWZ7dX2UKGgGR0CO1cTOgQHzaAdN6ANoCEdAlWcbYbsF+3V9lChoBkdAjNP61stTUGgHTegDaAhHQJV9s96kZaV1fZQoaAZHQI6NTKDCgsdoB03oA2gIR0CVfbW8h9srdX2UKGgGR0CL+Z9jPOY6aAdN6ANoCEdAlX23Rw6ySnV9lChoBkdAi/kTD4xk/mgHTegDaAhHQJV9uJQ+EAZ1fZQoaAZHQInJ6PQv6CVoB03oA2gIR0CVlDk1uR9xdX2UKGgGR0CK+oo73fygaAdN6ANoCEdAlZQ7MxGlRHV9lChoBkdAi21rZBcAzmgHTegDaAhHQJWUPQID5j91fZQoaAZHQIqwGTC+De1oB03oA2gIR0CVlD7pmmLtdX2UKGgGR0CIbsGi5/b1aAdN6ANoCEdAlary83++/XV9lChoBkdAiN4n0Cih4GgHTegDaAhHQJWq8+FDfFd1fZQoaAZHQImLwam4y45oB03oA2gIR0CVqvUAksz3dX2UKGgGR0CItUTSsr/baAdN6ANoCEdAlar2BOHnEHV9lChoBkdAiokIZIg/1WgHTegDaAhHQJXBgXhwVCZ1fZQoaAZHQI2mOR3eN1hoB03oA2gIR0CVwYMMqjJudX2UKGgGR0CNDtRmbsniaAdN6ANoCEdAlcGEpI+W4XV9lChoBkdAjBYoyKvV3GgHTegDaAhHQJXBhl8PWhB1fZQoaAZHQI2u80m+j/NoB03oA2gIR0CV18cnVoYfdX2UKGgGR0CM88EX+ERKaAdN6ANoCEdAldfH6yjYZnV9lChoBkdAjQruxjawlmgHTegDaAhHQJXXyO5rgwZ1fZQoaAZHQIsVMFjd56doB03oA2gIR0CV18nGbTc7dX2UKGgGR0CPV6p0fYBeaAdN6ANoCEdAlfZ46bONYXV9lChoBkdAjglm2CuloGgHTegDaAhHQJX2ecslLOB1fZQoaAZHQIpWcYl6Z6VoB03oA2gIR0CV9nqkuYhMdX2UKGgGR0COu25Yoy9FaAdN6ANoCEdAlfZ7YwqRU3V9lChoBkdAjsnHIQvpQmgHTegDaAhHQJYNSGQCCBh1fZQoaAZHQI7zkrbxmTVoB03oA2gIR0CWDUl6Z6UrdX2UKGgGR0CQmG8hLXcyaAdN6ANoCEdAlg1Kbe/HpHV9lChoBkdAkTGBBeHBUWgHTegDaAhHQJYNS2UjcEh1fZQoaAZHQI9EN56dDploB03oA2gIR0CWJGYhdMTOdX2UKGgGR0COQLDDTBqLaAdN6ANoCEdAliRm/Firk3V9lChoBkdAj1rrUb1h9mgHTegDaAhHQJYkZ6/qPfd1fZQoaAZHQI7R52GIsRRoB03oA2gIR0CWJGhb4agmdX2UKGgGR0CHyAeWfK6naAdN6ANoCEdAljuW5paibnV9lChoBkdAjiWYL1EmY2gHTegDaAhHQJY7mBRQ7911fZQoaAZHQI5/ne1rqMZoB03oA2gIR0CWO5lpGnXNdX2UKGgGR0CO/pcer+5waAdN6ANoCEdAljuayOaOP3V9lChoBkdAjT9KH446wWgHTegDaAhHQJZSXJQtSQ51fZQoaAZHQIyRgwmE5ABoB03oA2gIR0CWUl1schkidX2UKGgGR0CNRZ668QI2aAdN6ANoCEdAllJeG47Rv3V9lChoBkdAiMjEZiuuBGgHTegDaAhHQJZSXrC3w1B1fZQoaAZHQIgPob6xgRdoB03oA2gIR0CWaP1gH/tIdX2UKGgGR0B3I5yimEXdaAdN6ANoCEdAlmj+GbkOqnV9lChoBkdAg7LBBZ6lcmgHTegDaAhHQJZo/vUjLSx1fZQoaAZHQIi04B1cMVloB03oA2gIR0CWaQA08/2TdX2UKGgGR0CI3UOTaCcxaAdN6ANoCEdAln+KBqbjLnV9lChoBkdAictBYvFm4GgHTegDaAhHQJZ/iwC8vmJ1fZQoaAZHQIx/mVRk3CNoB03oA2gIR0CWf4vs7dSEdX2UKGgGR0CPMM7cwg1WaAdN6ANoCEdAln+NNN8E3nV9lChoBkdAjD0tDD0lJGgHTegDaAhHQJaWJRJmNBF1fZQoaAZHQJAyMzSCvoxoB03oA2gIR0CWliXumaYvdX2UKGgGR0CPsXOXVsk6aAdN6ANoCEdAlpYm+bmU4nV9lChoBkdAi/U0vGp++mgHTegDaAhHQJaWKC6H0sh1fZQoaAZHQIuDShHskY5oB03oA2gIR0CWrHGNrCWNdX2UKGgGR0CMbKCMglniaAdN6ANoCEdAlqxycPOIInV9lChoBkdAjci14X40uWgHTegDaAhHQJasc1P3ztl1fZQoaAZHQI3ROwosqaxoB03oA2gIR0CWrHP9UCJXdX2UKGgGR0CQq8+uNgjRaAdN6ANoCEdAlsMc9W6shnV9lChoBkdAjxjZG8VYZGgHTegDaAhHQJbDHcXWOIZ1fZQoaAZHQJDgHt4RmK9oB03oA2gIR0CWwx9PUKAsdX2UKGgGR0CNHtVBD5TIaAdN6ANoCEdAlsMgx8D0UXV9lChoBkdAjRbVGkN4JWgHTegDaAhHQJbZb+kxh2J1fZQoaAZHQI6FjI91U2loB03oA2gIR0CW2XC4jKPodX2UKGgGR0CNcFsu3+dcaAdN6ANoCEdAltlxddE9dXV9lChoBkdAiyiMMZxaPmgHTegDaAhHQJbZcmAskIJ1fZQoaAZHQIbNNYnv2GtoB03oA2gIR0CW8BuuieundX2UKGgGR0CLtyDe0ojOaAdN6ANoCEdAlvAcmOU+tHV9lChoBkdAire1DBuXNWgHTegDaAhHQJbwHYpUgjh1fZQoaAZHQI3isghbGFVoB03oA2gIR0CW8B5Xlr/LdX2UKGgGR0CB6TsniNsFaAdN6ANoCEdAlwayw4bS7XV9lChoBkdAf8hkKeCkGmgHTegDaAhHQJcGs/MW43F1fZQoaAZHQII3KBAfMfRoB03oA2gIR0CXBrVMmF8HdX2UKGgGR0B8ZSCPIXCTaAdN6ANoCEdAlwa2hIvrW3VlLg=="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 31250,
+    "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-HalfCheetahBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8a1137ea0cb62adbca668b000e97b0cb32eb240348d2d95919e69d7a7810a0e0
+size 54142

a2c-HalfCheetahBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6494a018f7a56600076422c0a444b35cf68ea8ca3d75393003df890096eadc59
+size 54910

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-5.10.147+-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 0x7f09cc1e3550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f09cc1e35e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f09cc1e3670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f09cc1e3700>", "_build": "<function ActorCriticPolicy._build at 0x7f09cc1e3790>", "forward": "<function ActorCriticPolicy.forward at 0x7f09cc1e3820>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f09cc1e38b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f09cc1e3940>", "_predict": "<function ActorCriticPolicy._predict at 0x7f09cc1e39d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f09cc1e3a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f09cc1e3af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f09cc1e3b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f09cc1e1150>"}, "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": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674935111349966054, "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:": "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": 31250, "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 sha256:082fa0a0357c5e15d59eeb196e70c32b48d5a50d3b2bbb1af1992f53d01f3f4c
+size 1026499

results.json

@@ -0,0 +1 @@
+{"mean_reward": 827.1972728048161, "std_reward": 108.14573460103892, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-28T20:13:01.256416"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ccffb9366022e8709d2e4e08ef83651528830ad7e0beaa066f8f115f4b125d96
+size 2056