I will present predictive state representations, or PSRs, a new class of predictive models for reinforcement learning. The key idea in PSRs is to use predictions of observable outcomes of tests or experiments the agent can do in its environment to represent the state of the environment. I will show that PSRs are more general than POMDPs and yet are at least as, and often more, compact than POMDPs. I will also present some results on learning PSR models from data and conclude with some reasons for optimism about PSR models as well as with directions for future work on PSRs.
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