Locally weighted least squares policy iteration for model-free learning in uncertain environments

摘要

This paper introduces Locally Weighted Least Squares Policy Iteration for learning approximate optimal control in settings where models of the dynamics and cost function are either unavailable or hard to obtain. Building on recent advances in Least Squares Temporal Difference Learning, the proposed approach is able to learn from data collected from interactions with a system, in order to build a global control policy based on localised models of the state-action value function. Evaluations are reported characterising learning performance for non-linear control problems including an under-powered pendulum swing-up task, and a robotic door-opening problem under different dynamical conditions.