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README.md

@@ -1,86 +0,0 @@
----
-library_name: stable-baselines3
-tags:
-- PandaReach-v1
-- deep-reinforcement-learning
-- reinforcement-learning
-- stable-baselines3
-model-index:
-- name: TQC
-  results:
-  - task:
-      type: reinforcement-learning
-      name: reinforcement-learning
-    dataset:
-      name: PandaReach-v1
-      type: PandaReach-v1
-    metrics:
-    - type: mean_reward
-      value: -2.10 +/- 0.70
-      name: mean_reward
-      verified: false
----
-
-# **TQC** Agent playing **PandaReach-v1**
-This is a trained model of a **TQC** agent playing **PandaReach-v1**
-using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
-and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
-
-The RL Zoo is a training framework for Stable Baselines3
-reinforcement learning agents,
-with hyperparameter optimization and pre-trained agents included.
-
-## Usage (with SB3 RL Zoo)
-
-RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
-SB3: https://github.com/DLR-RM/stable-baselines3<br/>
-SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
-
-Install the RL Zoo (with SB3 and SB3-Contrib):
-```bash
-pip install rl_zoo3
-```
-
-```
-# Download model and save it into the logs/ folder
-python -m rl_zoo3.load_from_hub --algo tqc --env PandaReach-v1 -orga qgallouedec -f logs/
-python -m rl_zoo3.enjoy --algo tqc --env PandaReach-v1  -f logs/
-```
-
-If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
-```
-python -m rl_zoo3.load_from_hub --algo tqc --env PandaReach-v1 -orga qgallouedec -f logs/
-python -m rl_zoo3.enjoy --algo tqc --env PandaReach-v1  -f logs/
-```
-
-## Training (with the RL Zoo)
-```
-python -m rl_zoo3.train --algo tqc --env PandaReach-v1 -f logs/
-# Upload the model and generate video (when possible)
-python -m rl_zoo3.push_to_hub --algo tqc --env PandaReach-v1 -f logs/ -orga qgallouedec
-```
-
-## Hyperparameters
-```python
-OrderedDict([('batch_size', 256),
-             ('buffer_size', 1000000),
-             ('ent_coef', 'auto'),
-             ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
-             ('gamma', 0.95),
-             ('learning_rate', 0.001),
-             ('learning_starts', 1000),
-             ('n_timesteps', 20000.0),
-             ('normalize', True),
-             ('policy', 'MultiInputPolicy'),
-             ('policy_kwargs', 'dict(net_arch=[64, 64], n_critics=1)'),
-             ('replay_buffer_class', 'HerReplayBuffer'),
-             ('replay_buffer_kwargs',
-              "dict( online_sampling=True, goal_selection_strategy='future', "
-              'n_sampled_goal=4 )'),
-             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
-```
-
-# Environment Arguments
-```python
-{'render': True}
-```