Towards Autonomous Reinforcement Learning: Automatic Setting of Hyper-parameters using Bayesian Optimization

TL;DR

This paper proposes an autonomous reinforcement learning framework that uses Bayesian optimization and bandit strategies to automatically tune hyper-parameters, improving learning efficiency in uncertain environments.

Contribution

It introduces a novel integration of Bayesian optimization with Gaussian processes and bandit methods for automatic hyper-parameter tuning in reinforcement learning.

Findings

Hyper-parameter optimization improves SARSA performance.

The framework reduces the need for manual tuning.

Demonstrated effectiveness on a gridworld example.

Abstract

With the increase of machine learning usage by industries and scientific communities in a variety of tasks such as text mining, image recognition and self-driving cars, automatic setting of hyper-parameter in learning algorithms is a key factor for achieving satisfactory performance regardless of user expertise in the inner workings of the techniques and methodologies. In particular, for a reinforcement learning algorithm, the efficiency of an agent learning a control policy in an uncertain environment is heavily dependent on the hyper-parameters used to balance exploration with exploitation. In this work, an autonomous learning framework that integrates Bayesian optimization with Gaussian process regression to optimize the hyper-parameters of a reinforcement learning algorithm, is proposed. Also, a bandits-based approach to achieve a balance between computational costs and decreasing uncertainty about the Q-values, is presented. A gridworld example is used to highlight how hyper-parameter configurations of a learning algorithm (SARSA) are iteratively improved based on two performance functions.