  Large transformer models powered by diverse data and model scale have
dominated natural language modeling and computer vision and pushed the frontier
of multiple AI areas. In reinforcement learning (RL), despite many efforts into
transformer-based policies, a key limitation, however, is that current
transformer-based policies cannot learn by directly combining information from
multiple sub-optimal trials. In this work, we address this issue using recently
proposed chain of hindsight to relabel experience, where we train a transformer
on a sequence of trajectory experience ascending sorted according to their
total rewards. Our method consists of relabelling target return of each
trajectory to the maximum total reward among in sequence of trajectories and
training an autoregressive model to predict actions conditioning on past
states, actions, rewards, target returns, and task completion tokens, the
resulting model, Agentic Transformer (AT), can learn to improve upon itself
both at training and test time. As we show on D4RL and ExoRL benchmarks, to the
best our knowledge, this is the first time that a simple transformer-based
model performs competitively with both temporal-difference and
imitation-learning-based approaches, even from sub-optimal data. Our Agentic
Transformer also shows a promising scaling trend that bigger models
consistently improve results.
