Jean-Raphael Gaglione

Advice-Guided Reinforcement Learning in a non-Markovian Environment

We study a class of reinforcement learning tasks in which the agent receives its reward for complex, temporally-extended behaviors sparsely. For such tasks, the problem is how to augment the state-space so as to make the reward function Markovian in an efficient way. While some existing solutions as…