readme: ---
  tags:
  - deep-reinforcement-learning
  - 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.
  
  
  ### Usage
  
  ```
  git clone https://huggingface.co/edbeeching/decision_transformer_atari
  conda env create -f conda_env.yml
  ```
  
  Then, you can use the model like this:
  
  ```python
  
  from decision_transform_atari import GPTConfig, GPT
  
  vocab_size = 4
  block_size = 90
  model_type = "reward_conditioned"
  timesteps = 2654
  
  mconf = GPTConfig(
      vocab_size,
      block_size,
      n_layer=6,
      n_head=8,
      n_embd=128,
      model_type=model_type,
      max_timestep=timesteps,
  )
  model = GPT(mconf)
  
  checkpoint_path = "checkpoints/Breakout_123.pth"  # or Pong, Qbert, Seaquest
  checkpoint = torch.load(checkpoint_path)
  model.load_state_dict(checkpoint)
  ```
  