Temporal Regularization in Markov Decision Process
This addresses instability issues in reinforcement learning applications, particularly in high-dimensional domains, but appears incremental as it builds on existing regularization techniques.
The paper tackles the problem of instability and high variance in reinforcement learning by introducing temporal regularization, which exploits smoothness in value estimates over trajectories, and demonstrates improvements in high-dimensional Atari games.
Several applications of Reinforcement Learning suffer from instability due to high variance. This is especially prevalent in high dimensional domains. Regularization is a commonly used technique in machine learning to reduce variance, at the cost of introducing some bias. Most existing regularization techniques focus on spatial (perceptual) regularization. Yet in reinforcement learning, due to the nature of the Bellman equation, there is an opportunity to also exploit temporal regularization based on smoothness in value estimates over trajectories. This paper explores a class of methods for temporal regularization. We formally characterize the bias induced by this technique using Markov chain concepts. We illustrate the various characteristics of temporal regularization via a sequence of simple discrete and continuous MDPs, and show that the technique provides improvement even in high-dimensional Atari games.