Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate
AbstractIn 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 meaningful reward-based tasks downstream.
How to Cite
Mutti, M., Pratissoli, L., & Restelli, M. (2021). Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate. Proceedings of the AAAI Conference on Artificial Intelligence, 35(10), 9028-9036. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17091
AAAI Technical Track on Machine Learning III