Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
ICML, pp. 6961-6971, 2019.
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
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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