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Creators/Authors contains: "Winnicki, Anna"

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  1. ; ; (, IEEE Transactions on Control of Network Systems)
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  2. ; ; ; (, Operations Research)
    In many applications of reinforcement learning, the underlying system dynamics are known, but computing the optimal policy is still difficult because the size of the state space can be enormously large. For example, Shannon famously estimated the number of states in chess to be approximately 10^120. To handle such enormous state space sizes, policy iteration algorithms make two major approximations; it is assumed that the value function lies in a lower-dimensional space and that only a few steps of a trajectory (called rollout) are used to evaluate a policy. Using a counterexample, we show that approximations can lead to the divergence of policy iteration. We show that sufficient lookahead in the policy improvement step mitigates this divergence and leads to algorithms with bounded errors. We also show that these errors can be controlled by appropriately choosing an appropriate amount of lookahead and rollout. 
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