Upload DDPG Panda Reach model for 100 000 timesteps

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachJointsDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: DDPG
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachJointsDense-v3
+      type: PandaReachJointsDense-v3
+    metrics:
+    - type: mean_reward
+      value: -21.06 +/- 6.60
+      name: mean_reward
+      verified: false
+---
+
+# **DDPG** Agent playing **PandaReachJointsDense-v3**
+This is a trained model of a **DDPG** agent playing **PandaReachJointsDense-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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+    "batch_size": 256,
+    "learning_starts": 100,
+    "tau": 0.005,
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+        "__module__": "stable_baselines3.her.her_replay_buffer",
+        "__firstlineno__": 15,
+        "__annotations__": "{'env': typing.Optional[stable_baselines3.common.vec_env.base_vec_env.VecEnv]}",
+        "__doc__": "\nHindsight Experience Replay (HER) buffer.\nPaper: https://arxiv.org/abs/1707.01495\n\nReplay 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",
+        "__init__": "<function HerReplayBuffer.__init__ at 0x12b4a5da0>",
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+        "add": "<function HerReplayBuffer.add at 0x12b4a60c0>",
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+        "truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x12b4a6480>",
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+            "ep_length",
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+            "goal_selection_strategy",
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+            "infos",
+            "n_sampled_goal"
+        ],
+        "__abstractmethods__": "frozenset()",
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+    },
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+}

ddpg-panda-reach-100k/policy.pth

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ddpg-panda-reach-100k/pytorch_variables.pth

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ddpg-panda-reach-100k/system_info.txt

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+- OS: macOS-15.3-arm64-arm-64bit-Mach-O Darwin Kernel Version 24.3.0: Thu Jan  2 20:24:23 PST 2025; root:xnu-11215.81.4~3/RELEASE_ARM64_T8122
+- Python: 3.13.1
+- Stable-Baselines3: 2.5.0
+- PyTorch: 2.6.0
+- GPU Enabled: False
+- Numpy: 2.2.3
+- Cloudpickle: 3.1.1
+- Gymnasium: 1.0.0
+- OpenAI Gym: 0.26.2

results.json

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+{"mean_reward": -21.0586356125772, "std_reward": 6.597103403222232, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-02-18T20:50:52.874923"}