Wasserstein Distance Maximizing Intrinsic Control [article]

Ishan Durugkar, Steven Hansen, Stephen Spencer, Volodymyr Mnih
<span title="2021-10-28">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper deals with the problem of learning a skill-conditioned policy that acts meaningfully in the absence of a reward signal. Mutual information based objectives have shown some success in learning skills that reach a diverse set of states in this setting. These objectives include a KL-divergence term, which is maximized by visiting distinct states even if those states are not far apart in the MDP. This paper presents an approach that rewards the agent for learning skills that maximize the
more &raquo; ... Wasserstein distance of their state visitation from the start state of the skill. It shows that such an objective leads to a policy that covers more distance in the MDP than diversity based objectives, and validates the results on a variety of Atari environments.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.15331v1">arXiv:2110.15331v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fuicl26mv5a5fbxcblzexu2mim">fatcat:fuicl26mv5a5fbxcblzexu2mim</a> </span>
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