Lambda‐Policy Iteration: A Review and a New Implementation
Dimitri P. Bertsekas · 2012
This chapter talks about Lambda-policy iteration, a method for exact and approximate dynamic programming. It is intermediate between the classical value iteration (VI) and the policy iteration (PI) methods, and it is closely related to optimistic (also known as modified) PI, whereby each policy evaluation is done approximately, using a finite number of VI. The chapter reviews the theory of the method and associated questions of bias and exploration arising in simulation-based cost function approximation. It then discusses various implementations, which offer advantages over well-established PI methods that use least squares policy evaluation (LSPE) (λ), least squares temporal differences (LSTD) (λ), or temporal difference (TD) (λ) for policy evaluation with cost function approximation. One of these implementations is based on a new simulation scheme, called geometric sampling, which uses multiple short trajectories rather than a single infinitely long trajectory. Controlled Vocabulary Terms cost reduction; iterative methods