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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: -6.30 +/- 1.79 |
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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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# 1 - 2 |
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env_id = "PandaPickAndPlace-v3" |
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env = gym.make(env_id) |
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# 4 |
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from stable_baselines3 import HerReplayBuffer, SAC |
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model = TQC(policy = "MultiInputPolicy", |
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env = env, |
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batch_size=2048, |
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gamma=0.95, |
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learning_rate=1e-4, |
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train_freq=64, |
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gradient_steps=64, |
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tau=0.05, |
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replay_buffer_class=HerReplayBuffer, |
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replay_buffer_kwargs=dict( |
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n_sampled_goal=4, |
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goal_selection_strategy="future", |
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), |
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policy_kwargs=dict( |
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net_arch=[512, 512, 512], |
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n_critics=2, |
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), |
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tensorboard_log=f"runs/{wandb_run.id}", |
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) |
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# 5 |
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model.learn(1_000_000, progress_bar=True, callback=WandbCallback(verbose=2)) |
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wandb_run.finish() |
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``` |
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Weights & Biases charts: https://wandb.ai/patonw/PandaPickAndPlace-v3/runs/w7lzlwnx/workspace?workspace=user-patonw |
