  Reinforcement learning (RL) is typically concerned with estimating stationary
policies or single-step models, leveraging the Markov property to factorize
problems in time. However, we can also view RL as a generic sequence modeling
problem, with the goal being to produce a sequence of actions that leads to a
sequence of high rewards. Viewed in this way, it is tempting to consider
whether high-capacity sequence prediction models that work well in other
domains, such as natural-language processing, can also provide effective
solutions to the RL problem. To this end, we explore how RL can be tackled with
the tools of sequence modeling, using a Transformer architecture to model
distributions over trajectories and repurposing beam search as a planning
algorithm. Framing RL as sequence modeling problem simplifies a range of design
decisions, allowing us to dispense with many of the components common in
offline RL algorithms. We demonstrate the flexibility of this approach across
long-horizon dynamics prediction, imitation learning, goal-conditioned RL, and
offline RL. Further, we show that this approach can be combined with existing
model-free algorithms to yield a state-of-the-art planner in sparse-reward,
long-horizon tasks.
