A survey of benchmarking frameworks for reinforcement learning

TL;DR

This survey reviews various reinforcement learning benchmarking frameworks, emphasizing their role in ensuring reproducibility, fair comparison, and standardization to advance research in the field.

Contribution

It provides a comprehensive overview of recent and widely used RL benchmarks, highlighting their implementation, tasks, and algorithm support to aid researchers.

Findings

Highlights the importance of benchmarks for reproducibility and fair comparison.

Summarizes recent popular RL benchmarking frameworks and their features.

Encourages standardization and wider adoption of benchmarking practices.

Abstract

Reinforcement learning has recently experienced increased prominence in the machine learning community. There are many approaches to solving reinforcement learning problems with new techniques developed constantly. When solving problems using reinforcement learning, there are various difficult challenges to overcome. To ensure progress in the field, benchmarks are important for testing new algorithms and comparing with other approaches. The reproducibility of results for fair comparison is therefore vital in ensuring that improvements are accurately judged. This paper provides an overview of different contributions to reinforcement learning benchmarking and discusses how they can assist researchers to address the challenges facing reinforcement learning. The contributions discussed are the most used and recent in the literature. The paper discusses the contributions in terms of implementation, tasks and provided algorithm implementations with benchmarks. The survey aims to bring attention to the wide range of reinforcement learning benchmarking tasks available and to encourage research to take place in a standardised manner. Additionally, this survey acts as an overview for researchers not familiar with the different tasks that can be used to develop and test new reinforcement learning algorithms.