Provably Efficient Maximum Entropy Exploration
This addresses the challenge of intrinsic exploration for agents in unknown environments, offering a foundational approach with theoretical guarantees, though it is incremental in applying known optimization methods to this context.
The paper tackles the problem of efficient exploration in Markov Decision Processes without reward signals by optimizing objectives based on state-visitation frequencies, such as maximizing entropy for uniform state distribution. It provides a provably efficient algorithm with sample and computational complexity guarantees in tabular settings.
Suppose an agent is in a (possibly unknown) Markov Decision Process in the absence of a reward signal, what might we hope that an agent can efficiently learn to do? This work studies a broad class of objectives that are defined solely as functions of the state-visitation frequencies that are induced by how the agent behaves. For example, one natural, intrinsically defined, objective problem is for the agent to learn a policy which induces a distribution over state space that is as uniform as possible, which can be measured in an entropic sense. We provide an efficient algorithm to optimize such such intrinsically defined objectives, when given access to a black box planning oracle (which is robust to function approximation). Furthermore, when restricted to the tabular setting where we have sample based access to the MDP, our proposed algorithm is provably efficient, both in terms of its sample and computational complexities. Key to our algorithmic methodology is utilizing the conditional gradient method (a.k.a. the Frank-Wolfe algorithm) which utilizes an approximate MDP solver.