A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/2110.15331v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
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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<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>
more »... 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.
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