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原文摘要

In this paper, we present a Koopman autoencoder-based least-squares policy iteration (KAE-LSPI) algorithm for reinforcement learning (RL). The KAE-LSPI algorithm is based on reformulating the so-called least-squares fixed-point approximation method in terms of extended dynamic mode decomposition (EDMD), thereby enabling automatic feature learning via the Koopman autoencoder (KAE) framework. The approach is motivated by the lack of a systematic procedure for selecting features or kernels in linear RL techniques. Unlike previous approaches, no features or kernels need to be fixed a priori in our method. We discuss the theoretical convergence of KAE-LSPI under suitable assumptions, similar to kernel-based least-squares policy iteration (KLSPI). We empirically compare our method with two existing algorithms: least-squares policy iteration (LSPI) and KLSPI. In particular, for the chainwalk problem, the resulting policies are either optimal or nearly optimal for both three approaches. For the cart-pole system, KAE-LSPI outperforms the other two approaches in terms of policy performance.