A survey of benchmarks for reinforcement learning algorithms

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. \par 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. \par 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.

How to Design Reinforcement Learning Methods for the Edge: An Integrated Approach toward Intelligent Decision Making

“…At a macrolevel, some researchers have made achievements, such as Stapelberg et al, who discussed existing benchmark tasks in reinforcement learning to provide an overview for beginners or researchers with different task requirements [46]. Aslanides et al attempted to organize existing results of general reinforcement learning methods [47].…”

Reinforcement Learning for Pick and Place Operations in Robotics: A Survey

“…OpenAI (https://gym.openai.com/, accessed on 15 August 2021) has attempted to standardize the benchmarking environments for RL by creating open access simulations for testing control strategies. Cartpole is an example of a classic control problem found on OpenAI, in which the agent can make a command (left or right) and receive a low dimensional feedback signal (position and angular velocity) [20]. Compared to this simple benchmark, robotic arm manipulation is foundationally more difficult.…”