model

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.gitattributes +1 -0
README.md +40 -0
config.json +1 -0
humanoid-v5-TQC-medium.zip +3 -0
humanoid-v5-TQC-medium/_stable_baselines3_version +1 -0
humanoid-v5-TQC-medium/actor.optimizer.pth +3 -0
humanoid-v5-TQC-medium/critic.optimizer.pth +3 -0
humanoid-v5-TQC-medium/data +126 -0
humanoid-v5-TQC-medium/ent_coef_optimizer.pth +3 -0
humanoid-v5-TQC-medium/policy.pth +3 -0
humanoid-v5-TQC-medium/pytorch_variables.pth +3 -0
humanoid-v5-TQC-medium/system_info.txt +8 -0
replay.mp4 +3 -0
results.json +1 -0

.gitattributes CHANGED Viewed

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 *.zip filter=lfs diff=lfs merge=lfs -text
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README.md ADDED Viewed

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+---
+library_name: stable-baselines3
+tags:
+- Humanoid-v5
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: TQC
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Humanoid-v5
+      type: Humanoid-v5
+    metrics:
+    - type: mean_reward
+      value: 8021.95 +/- 912.19
+      name: mean_reward
+      verified: false
+---
+# **TQC** Agent playing **Humanoid-v5**
+This is a trained model of a **TQC** agent playing **Humanoid-v5**
+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
+...
+```

config.json ADDED Viewed

	@@ -0,0 +1 @@

+ {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVKgAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMCVRRQ1BvbGljeZSTlC4=", "__module__": "sb3_contrib.tqc.policies", "__annotations__": "{'actor': <class 'sb3_contrib.tqc.policies.Actor'>, 'critic': <class 'sb3_contrib.tqc.policies.Critic'>, 'critic_target': <class 'sb3_contrib.tqc.policies.Critic'>}", "__doc__": "\n Policy class (with both actor and critic) for TQC.\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 use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the feature 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 :param n_quantiles: Number of quantiles for the critic.\n :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ", "__init__": "<function TQCPolicy.__init__ at 0x7faa74b97f60>", "_build": "<function TQCPolicy._build at 0x7faa74bc0040>", "_get_constructor_parameters": "<function TQCPolicy._get_constructor_parameters at 0x7faa74bc00e0>", "reset_noise": "<function TQCPolicy.reset_noise at 0x7faa74bc0180>", "make_actor": "<function TQCPolicy.make_actor at 0x7faa74bc0220>", "make_critic": "<function TQCPolicy.make_critic at 0x7faa74bc02c0>", "forward": "<function TQCPolicy.forward at 0x7faa74bc0360>", "_predict": "<function TQCPolicy._predict at 0x7faa74bc0400>", "set_training_mode": "<function TQCPolicy.set_training_mode at 0x7faa74bc04a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7faa74ba2240>"}, "verbose": 0, "policy_kwargs": {"use_sde": false}, "num_timesteps": 4950000, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": 0, "action_noise": null, "start_time": 1739143247756297255, "learning_rate": 0.0003, "tensorboard_log": "runs/0", "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": 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humanoid-v5-TQC-medium.zip ADDED Viewed

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+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVKgAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMCVRRQ1BvbGljeZSTlC4=",
+        "__module__": "sb3_contrib.tqc.policies",
+        "__annotations__": "{'actor': <class 'sb3_contrib.tqc.policies.Actor'>, 'critic': <class 'sb3_contrib.tqc.policies.Critic'>, 'critic_target': <class 'sb3_contrib.tqc.policies.Critic'>}",
+        "__doc__": "\n    Policy class (with both actor and critic) for TQC.\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 use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the feature 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    :param n_quantiles: Number of quantiles for the critic.\n    :param n_critics: Number of critic networks to create.\n    :param share_features_extractor: Whether to share or not the features extractor\n        between the actor and the critic (this saves computation time)\n    ",
+        "__init__": "<function TQCPolicy.__init__ at 0x7faa74b97f60>",
+        "_build": "<function TQCPolicy._build at 0x7faa74bc0040>",
+        "_get_constructor_parameters": "<function TQCPolicy._get_constructor_parameters at 0x7faa74bc00e0>",
+        "reset_noise": "<function TQCPolicy.reset_noise at 0x7faa74bc0180>",
+        "make_actor": "<function TQCPolicy.make_actor at 0x7faa74bc0220>",
+        "make_critic": "<function TQCPolicy.make_critic at 0x7faa74bc02c0>",
+        "forward": "<function TQCPolicy.forward at 0x7faa74bc0360>",
+        "_predict": "<function TQCPolicy._predict at 0x7faa74bc0400>",
+        "set_training_mode": "<function TQCPolicy.set_training_mode at 0x7faa74bc04a0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7faa74ba2240>"
+    },
+    "verbose": 0,
+    "policy_kwargs": {
+        "use_sde": false
+    },
+    "num_timesteps": 4950000,
+    "_total_timesteps": 5000000,
+    "_num_timesteps_at_start": 0,
+    "seed": 0,
+    "action_noise": null,
+    "start_time": 1739143247756297255,
+    "learning_rate": 0.0003,
+    "tensorboard_log": "runs/0",
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": 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+    },
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+    },
+    "_episode_num": 9906,
+    "use_sde": false,
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1	+ {"mean_reward": 8021.94777762, "std_reward": 912.1852202495186, "is_deterministic": true, "n_eval_episodes": 1000, "eval_datetime": "2025-02-10T21:35:17.326421"}