Subiendo modelo entrenado para FetchPickAndPlace-v4 con replay video

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- FetchPickAndPlace-v4
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: SAC
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: FetchPickAndPlace-v4
+      type: FetchPickAndPlace-v4
+    metrics:
+    - type: mean_reward
+      value: -9.70 +/- 4.17
+      name: mean_reward
+      verified: false
+---
+
+# **SAC** Agent playing **FetchPickAndPlace-v4**
+This is a trained model of a **SAC** agent playing **FetchPickAndPlace-v4**
+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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+            4
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+        "bounded_below": "[ True  True  True  True]",
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+        "bounded_above": "[ True  True  True  True]",
+        "low_repr": "-1.0",
+        "high_repr": "1.0",
+        "_np_random": "Generator(PCG64)"
+    },
+    "n_envs": 1,
+    "buffer_size": 1000000,
+    "batch_size": 512,
+    "learning_starts": 100,
+    "tau": 0.05,
+    "gamma": 0.95,
+    "gradient_steps": 1,
+    "optimize_memory_usage": false,
+    "replay_buffer_class": {
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+        "__module__": "stable_baselines3.her.her_replay_buffer",
+        "__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    ",
+        "__init__": "<function HerReplayBuffer.__init__ at 0x000001BDEC6FC720>",
+        "__getstate__": "<function HerReplayBuffer.__getstate__ at 0x000001BDEC6FC7C0>",
+        "__setstate__": "<function HerReplayBuffer.__setstate__ at 0x000001BDEC6FC860>",
+        "set_env": "<function HerReplayBuffer.set_env at 0x000001BDEC6FC900>",
+        "add": "<function HerReplayBuffer.add at 0x000001BDEC6FCA40>",
+        "_compute_episode_length": "<function HerReplayBuffer._compute_episode_length at 0x000001BDEC6FCAE0>",
+        "sample": "<function HerReplayBuffer.sample at 0x000001BDEC6FCB80>",
+        "_get_real_samples": "<function HerReplayBuffer._get_real_samples at 0x000001BDEC6FCC20>",
+        "_get_virtual_samples": "<function HerReplayBuffer._get_virtual_samples at 0x000001BDEC6FCCC0>",
+        "_sample_goals": "<function HerReplayBuffer._sample_goals at 0x000001BDEC6FCD60>",
+        "truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x000001BDEC6FCE00>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x000001BDEC7085C0>"
+    },
+    "replay_buffer_kwargs": {
+        "n_sampled_goal": 4,
+        "goal_selection_strategy": "future"
+    },
+    "n_steps": 1,
+    "train_freq": {
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+    },
+    "use_sde_at_warmup": false,
+    "target_entropy": -4.0,
+    "ent_coef": "auto",
+    "target_update_interval": 1,
+    "lr_schedule": {
+        ":type:": "<class 'stable_baselines3.common.utils.FloatSchedule'>",
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+        "value_schedule": "ConstantSchedule(val=0.001)"
+    },
+    "batch_norm_stats": [],
+    "batch_norm_stats_target": []
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+- OS: Windows-11-10.0.22000-SP0 10.0.22000
+- Python: 3.12.3
+- Stable-Baselines3: 2.7.0
+- PyTorch: 2.7.1+cu118
+- GPU Enabled: True
+- Numpy: 2.2.6
+- Cloudpickle: 3.1.1
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