Optimism in Reinforcement Learning with Generalized Linear Function Approximation

international conference on learning representations, 2021.

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A provably efficient (statistically and computationally) algorithm for reinforcement learning with generalized linear function approximation and no explicit dynamics assumptions.

Abstract:

We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity assumption that we call ``optimistic closure,\u0027\u0027 which is strictly weaker than assumptions from prior analyses for the linear setting. With optimistic c...More

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