Beyond Trial and Error in Reinforcement Learning

Abstract

In this work, we address the trial-and-error nature of modern reinforcement learning (RL) methods by investigating approaches inspired by human cognition. By enhancing state representations and advancing causal reasoning and planning, we aim to improve RL performance, robustness, and explainability. Through diverse examples, we showcase the potential of these approaches to improve RL agents.