Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning

Abstract

This paper investigates a type of instability that is linked to the greedy policy improvement in approximated reinforcement learning. We show empirically that non-deterministic policy improvement can stabilize methods like LSPI by controlling the improvements' stochasticity. Additionally we show that a suitable representation of the value function also stabilizes the solution to some degree. The presented approach is simple and should also be easily transferable to more sophisticated algorithms like deep reinforcement learning.

This paper has been presented at the 13th European Workshop on Reinforcement Learning (EWRL 2016) on the 3rd and 4th of December 2016 in Barcelona, Spain