Exploration versus exploitation in reinforcement learning: a stochastic control approach

TL;DR

This paper models the exploration-exploitation trade-off in continuous-time reinforcement learning using a stochastic control framework, revealing that optimal exploration strategies are Gaussian and analyzing their properties.

Contribution

It introduces an entropy-regularized formulation for exploration in continuous-time RL and characterizes the optimal Gaussian control distribution in the linear-quadratic setting.

Findings

Optimal exploration control is Gaussian, with mean and variance representing exploitation and exploration.

Less exploration is needed in more random environments, indicating more learning opportunities.

The cost of exploration is proportional to the entropy regularization weight and inversely proportional to the discount rate.

Abstract

We consider reinforcement learning (RL) in continuous time and study the problem of achieving the best trade-off between exploration of a black box environment and exploitation of current knowledge. We propose an entropy-regularized reward function involving the differential entropy of the distributions of actions, and motivate and devise an exploratory formulation for the feature dynamics that captures repetitive learning under exploration. The resulting optimization problem is a revitalization of the classical relaxed stochastic control. We carry out a complete analysis of the problem in the linear--quadratic (LQ) setting and deduce that the optimal feedback control distribution for balancing exploitation and exploration is Gaussian. This in turn interprets and justifies the widely adopted Gaussian exploration in RL, beyond its simplicity for sampling. Moreover, the exploitation and exploration are captured, respectively and mutual-exclusively, by the mean and variance of the Gaussian distribution. We also find that a more random environment contains more learning opportunities in the sense that less exploration is needed. We characterize the cost of exploration, which, for the LQ case, is shown to be proportional to the entropy regularization weight and inversely proportional to the discount rate. Finally, as the weight of exploration decays to zero, we prove the convergence of the solution of the entropy-regularized LQ problem to the one of the classical LQ problem.