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…



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

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