RL + Transformer = A General-Purpose Problem Solver
Authors: name: Micah Rentschler, name: Jesse Roberts

What if artificial intelligence could not only solve problems for which it
was trained but also learn to teach itself to solve new problems (i.e.,
meta-learn)? In this study, we demonstrate that a pre-trained transformer
fine-tuned with reinforcement learning over multiple episodes develops the
ability to solve problems that it has never encountered before - an emergent
ability called In-Context Reinforcement Learning (ICRL). This powerful
meta-learner not only excels in solving unseen in-distribution environments
with remarkable sample efficiency, but also shows strong performance in
out-of-distribution environments. In addition, we show that it exhibits
robustness to the quality of its training data, seamlessly stitches together
behaviors from its context, and adapts to non-stationary environments. These
behaviors demonstrate that an RL-trained transformer can iteratively improve
upon its own solutions, making it an excellent general-purpose problem solver.