HOMER: Provable Exploration in Reinforcement Learning

This week at ICML 2020, Mikael Henaff, Akshay Krishnamurthy, John Langford and I have a paper presenting a new reinforcement learning (RL) algorithm called HOMER that addresses three main problems in real-world RL problem: (i) exploration, (ii) decoding latent dynamics, and (iii) optimizing a given reward function. ArXiv version of the paper can be found here, and the ICML version would be released soon.

The paper is a bit mathematically heavy in nature and this post is an attempt to distil the key findings. We will also be following up…

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Machine learning and NLP Researcher at Microsoft Research, New York. https://dipendramisra.com/.

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