| ---
|
| tags:
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| - deep-reinforcement-learning
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| - reinforcement-learning
|
|
|
| ---
|
|
|
| Find here pretrained model weights for the [Decision Transformer] (https://github.com/kzl/decision-transformer).
|
| Weights are available for 4 Atari games: Breakout, Pong, Qbert and Seaquest. Found in the checkpoints directory.
|
| We share models trained for one seed (123), whereas the paper contained weights for 3 random seeds.
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|
|
|
|
| ### Usage
|
|
|
| ```
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| git clone https://huggingface.co/edbeeching/decision_transformer_atari
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| conda env create -f conda_env.yml
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| ```
|
|
|
| Then, you can use the model like this:
|
|
|
| ```python
|
|
|
| from decision_transform_atari import GPTConfig, GPT
|
|
|
| vocab_size = 4
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| block_size = 90
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| model_type = "reward_conditioned"
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| timesteps = 2654
|
|
|
| mconf = GPTConfig(
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| vocab_size,
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| block_size,
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| n_layer=6,
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| n_head=8,
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| n_embd=128,
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| model_type=model_type,
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| max_timestep=timesteps,
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| )
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| model = GPT(mconf)
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|
|
| checkpoint_path = "checkpoints/Breakout_123.pth" # or Pong, Qbert, Seaquest
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| checkpoint = torch.load(checkpoint_path)
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| model.load_state_dict(checkpoint)
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| ```
|
|
|
