Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning

ICML, pp. 6961-6971, 2019.

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We provide a solution for a significant sub-class of problems known as Block Markov Decision Processes, in which the agent operates directly on rich observations that are generated from a small number of unobserved latent states

Abstract:

We present an algorithm, HOMER, for exploration and reinforcement learning in rich observation environments that are summarizable by an unknown latent state space. The algorithm interleaves representation learning to identify a new notion of kinematic state abstraction with strategic exploration to reach new states using the learned abs...More

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