HOMER: Provable Exploration in Reinforcement Learning
Last week at ICML 2020, Mikael Henaff, Akshay Krishnamurthy, John Langford and I had a paper on a new reinforcement learning (RL) algorithm that solves three key problems in RL: (i) global exploration, (ii) decoding latent dynamics, and (iii) optimizing a given reward function. Our ICML poster is here. The paper is a bit mathematically heavy in nature so this post is an attempt to distill the key findings. We will also be following up soon with a new codebase release (more on […]
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