Actively Learning Costly Reward Functions for Reinforcement Learning

TL;DR

This paper introduces ACRL, an active learning approach that models costly rewards with neural networks to significantly speed up reinforcement learning in real-world applications where reward evaluation is expensive.

Contribution

The paper presents ACRL, a novel method that replaces costly reward evaluations with learned models, enabling faster training in complex real-world environments.

Findings

ACRL accelerates training by orders of magnitude in real-world tasks.

It enables reinforcement learning in domains with expensive reward evaluations.

ACRL finds non-trivial solutions in chemistry, materials science, and engineering.

Abstract

Transfer of recent advances in deep reinforcement learning to real-world applications is hindered by high data demands and thus low efficiency and scalability. Through independent improvements of components such as replay buffers or more stable learning algorithms, and through massively distributed systems, training time could be reduced from several days to several hours for standard benchmark tasks. However, while rewards in simulated environments are well-defined and easy to compute, reward evaluation becomes the bottleneck in many real-world environments, e.g., in molecular optimization tasks, where computationally demanding simulations or even experiments are required to evaluate states and to quantify rewards. Therefore, training might become prohibitively expensive without an extensive amount of computational resources and time. We propose to alleviate this problem by replacing costly ground-truth rewards with rewards modeled by neural networks, counteracting non-stationarity of state and reward distributions during training with an active learning component. We demonstrate that using our proposed ACRL method (Actively learning Costly rewards for Reinforcement Learning), it is possible to train agents in complex real-world environments orders of magnitudes faster. By enabling the application of reinforcement learning methods to new domains, we show that we can find interesting and non-trivial solutions to real-world optimization problems in chemistry, materials science and engineering.