Commit sac-100k-HER model

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: sac
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v3
+      type: PandaReachDense-v3
+    metrics:
+    - type: mean_reward
+      value: -11.68 +/- 8.13
+      name: mean_reward
+      verified: false
+---
+
+# **sac** Agent playing **PandaReachDense-v3**
+This is a trained model of a **sac** agent playing **PandaReachDense-v3**
+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

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+    "buffer_size": 10000,
+    "batch_size": 256,
+    "learning_starts": 1000,
+    "tau": 0.005,
+    "gamma": 0.99,
+    "gradient_steps": 1,
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+        "__annotations__": "{'env': typing.Optional[stable_baselines3.common.vec_env.base_vec_env.VecEnv]}",
+        "__doc__": "\n    Hindsight Experience Replay (HER) buffer.\n    Paper: https://arxiv.org/abs/1707.01495\n\n    Replay buffer for sampling HER (Hindsight Experience Replay) transitions.\n\n    .. note::\n\n      Compared to other implementations, the ``future`` goal sampling strategy is inclusive:\n      the current transition can be used when re-sampling.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param env: The training environment\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    :param n_sampled_goal: Number of virtual transitions to create per real transition,\n        by sampling new goals.\n    :param goal_selection_strategy: Strategy for sampling goals for replay.\n        One of ['episode', 'final', 'future']\n    :param copy_info_dict: Whether to copy the info dictionary and pass it to\n        ``compute_reward()`` method.\n        Please note that the copy may cause a slowdown.\n        False by default.\n    ",
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+        "sample": "<function HerReplayBuffer.sample at 0x7f1d5c7da560>",
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+        "_get_virtual_samples": "<function HerReplayBuffer._get_virtual_samples at 0x7f1d5c7da680>",
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+        "truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x7f1d5c7da7a0>",
+        "__abstractmethods__": "frozenset()",
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+    },
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+    "batch_norm_stats_target": []
+}

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+- Stable-Baselines3: 2.2.1
+- PyTorch: 2.1.0
+- GPU Enabled: False
+- Numpy: 1.26.3
+- Cloudpickle: 2.2.1
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