Decision Transformer: Reinforcement Learning via Sequence Modelling - Paper Review (Presentation Slides)

Abstract

Recent studies have shown that transformers can model high-dimensional distributions of semantic concepts at scale, opening up the intriguing possibility of formalizing sequential decision-making problems as reinforcement learning (RL). New research from a UC Berkeley, Facebook AI Research and Google Brain explores whether generative trajectory modelling — i.e. modelling the joint distribution of a sequence of states, actions, and rewards — could serve as a replacement for conventional RL algorithms. We present a paper review of this research

Keywords: Transformers, Reinforcement Learning

JEL Classification: G11