Decision Transformer: Reinforcement Learning via Sequence Modeling

Code

• github.com/kzl/decision-transformer
  OfficialIn paperpytorch★ 2,778
• github.com/opendilab/DI-engine
  pytorch★ 3,606
• github.com/takuseno/d3rlpy
  pytorch★ 1,647
• github.com/corl-team/CORL
  jax★ 632
• github.com/facebookresearch/salina
  jax★ 428
• github.com/nikhilbarhate99/min-decision-transformer
  pytorch★ 288
• github.com/typoverflow/OfflineRL-Lib
  pytorch★ 77
• github.com/ml-jku/l2m
  pytorch★ 61
• github.com/zzmtsvv/rl_task
  pytorch★ 58
• github.com/LAS1520/Gato-A-Generalist-Agent
  pytorch★ 44

Abstract

We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. This allows us to draw upon the simplicity and scalability of the Transformer architecture, and associated advances in language modeling such as GPT-x and BERT. In particular, we present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling. Unlike prior approaches to RL that fit value functions or compute policy gradients, Decision Transformer simply outputs the optimal actions by leveraging a causally masked Transformer. By conditioning an autoregressive model on the desired return (reward), past states, and actions, our Decision Transformer model can generate future actions that achieve the desired return. Despite its simplicity, Decision Transformer matches or exceeds the performance of state-of-the-art model-free offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks.

Tasks

Benchmark Results

Reproductions