Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate
This addresses the challenge of efficient exploration for reinforcement learning agents in continuous-control settings, offering a novel approach that is incremental over prior methods.
The paper tackles the problem of learning an optimal task-agnostic exploration policy in reward-free environments by proposing to maximize the entropy of the state distribution, and it introduces MEPOL, a model-free algorithm that achieves this and facilitates downstream task learning in high-dimensional domains.
In a reward-free environment, what is a suitable intrinsic objective for an agent to pursue so that it can learn an optimal task-agnostic exploration policy? In this paper, we argue that the entropy of the state distribution induced by finite-horizon trajectories is a sensible target. Especially, we present a novel and practical policy-search algorithm, Maximum Entropy POLicy optimization (MEPOL), to learn a policy that maximizes a non-parametric, $k$-nearest neighbors estimate of the state distribution entropy. In contrast to known methods, MEPOL is completely model-free as it requires neither to estimate the state distribution of any policy nor to model transition dynamics. Then, we empirically show that MEPOL allows learning a maximum-entropy exploration policy in high-dimensional, continuous-control domains, and how this policy facilitates learning a variety of meaningful reward-based tasks downstream.