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Browse files- .gitattributes +1 -0
- README.md +37 -0
- config.json +1 -0
- replay.mp4 +3 -0
- results.json +1 -0
- tqc-PandaPickAndPlace-v3.zip +3 -0
- tqc-PandaPickAndPlace-v3/_stable_baselines3_version +1 -0
- tqc-PandaPickAndPlace-v3/actor.optimizer.pth +3 -0
- tqc-PandaPickAndPlace-v3/critic.optimizer.pth +3 -0
- tqc-PandaPickAndPlace-v3/data +117 -0
- tqc-PandaPickAndPlace-v3/ent_coef_optimizer.pth +3 -0
- tqc-PandaPickAndPlace-v3/policy.pth +3 -0
- tqc-PandaPickAndPlace-v3/pytorch_variables.pth +3 -0
- tqc-PandaPickAndPlace-v3/system_info.txt +9 -0
- vec_normalize.pkl +3 -0
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README.md
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---
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library_name: stable-baselines3
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tags:
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- PandaPickAndPlace-v3
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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model-index:
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- name: TQC
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results:
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- task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: PandaPickAndPlace-v3
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type: PandaPickAndPlace-v3
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metrics:
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- type: mean_reward
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value: -45.00 +/- 15.00
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name: mean_reward
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verified: false
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---
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# **TQC** Agent playing **PandaPickAndPlace-v3**
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This is a trained model of a **TQC** agent playing **PandaPickAndPlace-v3**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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## Usage (with Stable-baselines3)
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TODO: Add your code
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```python
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from stable_baselines3 import ...
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from huggingface_sb3 import load_from_hub
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...
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```
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config.json
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{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVMQAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu", "__module__": "sb3_contrib.tqc.policies", "__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 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 MultiInputPolicy.__init__ at 0x7ffa471a2de0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ffa46fb2f40>"}, "verbose": 1, "policy_kwargs": {"use_sde": false}, "num_timesteps": 100000, 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"__doc__": "\n Dict Replay buffer used in off-policy algorithms like SAC/TD3.\n Extends the ReplayBuffer to use dictionary observations\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 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 ",
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tqc-PandaPickAndPlace-v3/ent_coef_optimizer.pth
ADDED
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tqc-PandaPickAndPlace-v3/policy.pth
ADDED
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ADDED
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ADDED
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|
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|
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vec_normalize.pkl
ADDED
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