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• W3133860714 abstract "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." @default.
• W3133860714 created "2021-03-15" @default.
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• W3133860714 date "2020-07-09" @default.
• W3133860714 modified "2023-09-23" @default.
• W3133860714 title "Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate" @default.
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• W3133860714 hasPublicationYear "2020" @default.
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