Decision Transformer: Reinforcement Learning via Sequence Modeling (Research Paper Explained)

#decisiontransformer #reinforcementlearning #transformer

Proper credit assignment over long timespans is a fundamental problem in reinforcement learning. Even methods designed to combat this problem, such as TD-learning, quickly reach their limits when rewards are sparse or noisy. This paper reframes offline reinforcement learning as a pure sequence modeling problem, with the actions being sampled conditioned on the given history and desired future rewards. This allows the authors to use recent advances in sequence modeling using Transformers and achieve competitive results in Offline RL benchmarks.

OUTLINE:

0:00 - Intro & Overview

4:15 - Offline Reinforcement Learning

10:10 - Transformers in RL

14:25 - Value Functions and Temporal Difference Learning

20:25 - Sequence Modeling and Reward-to-go

27:20 - Why this is ideal for offline RL

31:30 - The context length problem

34:35 - Toy example: Shortest path from random walks

41:00 - Discount factors

45:50 - Experimental Results

49:25 - Do you need to know the best possible reward?

52:15 - Key-to-door toy experiment

56:00 - Comments & Conclusion

Paper: https://arxiv.org/abs/2106.01345

Website: https://sites.google.com/berkeley.edu...

Code: https://github.com/kzl/decision-trans...

Abstract:

We present 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.

Authors: Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch

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