Model-Based Bayesian Exploration

TL;DR

This paper introduces a model-based Bayesian approach to reinforcement learning that explicitly represents uncertainty in environment models to improve exploration strategies by estimating the value of information.

Contribution

It proposes a novel method of representing and reasoning about uncertainty in model parameters to guide exploration in reinforcement learning.

Findings

Effective estimation of value of information improves exploration decisions.

Bayesian uncertainty modeling enhances learning efficiency.

Method outperforms traditional exploration strategies in benchmark tests.

Abstract

Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classical notion of Value of Information - the expected improvement in future decision quality arising from the information acquired by exploration. Estimating this quantity requires an assessment of the agent's uncertainty about its current value estimates for states. In this paper we investigate ways of representing and reasoning about this uncertainty in algorithms where the system attempts to learn a model of its environment. We explicitly represent uncertainty about the parameters of the model and build probability distributions over Q-values based on these. These distributions are used to compute a myopic approximation to the value of information for each action and hence to select the action that best balances exploration and exploitation.