Decision Transformer

Clean and readable code for Decision Transformer: Reinforcement Learning via Sequence Modeling.

Notable difference from official implementation are:

• Simple GPT implementation (causal transformer)
• Uses PyTorch's Dataset and Dataloader class and removes redundant computations for calculating rewards to go and state normalization for efficient training

Instructions

Results

Note that these results are mean and variance for 3 random seeds obtained by after only 20k updates while the official models are trained to saturation for 100k updates.

References

• official code and paper
• minimal GPT (causal transformer) tweet and colab notebook