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arXiv · 1905.10404

InfoRL: Interpretable Reinforcement Learning using Information Maximization

Aadil Hayat·Utsav Singh·Vinay P. Namboodiri
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Abstract

Recent advances in reinforcement learning have proved that given an environment we can learn to perform a task in that environment if we have access to some form of a reward function (dense, sparse or derived from IRL). But most of the algorithms focus on learning a single best policy to perform a given set of tasks. In this paper, we focus on an algorithm that learns to not just perform a task but different ways to perform the same task. As we know when the environment is complex enough there always exists multiple ways to perform a task. We show that using the concept of information maximization it is possible to learn latent codes for discovering multiple ways to perform any given task in an environment.

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Aadil Hayat, Utsav Singh, Vinay P. Namboodiri. 2019-05-24. InfoRL: Interpretable Reinforcement Learning using Information Maximization. https://arxiv.org/abs/1905.10404

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