Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning
This provides a domain-specific benchmark for researchers developing RL methods for real-world deployment, though it is incremental as it builds on existing RL frameworks.
The authors tackled the lack of realistic benchmarks for reinforcement learning (RL) in real-world applications by introducing Gym4ReaL, a suite of environments that expose algorithms to challenges like large state-action spaces and non-stationarity, and found that standard RL algorithms remain competitive against rule-based benchmarks.
In recent years, \emph{Reinforcement Learning} (RL) has made remarkable progress, achieving superhuman performance in a wide range of simulated environments. As research moves toward deploying RL in real-world applications, the field faces a new set of challenges inherent to real-world settings, such as large state-action spaces, non-stationarity, and partial observability. Despite their importance, these challenges are often underexplored in current benchmarks, which tend to focus on idealized, fully observable, and stationary environments, often neglecting to incorporate real-world complexities explicitly. In this paper, we introduce \texttt{Gym4ReaL}, a comprehensive suite of realistic environments designed to support the development and evaluation of RL algorithms that can operate in real-world scenarios. The suite includes a diverse set of tasks that expose algorithms to a variety of practical challenges. Our experimental results show that, in these settings, standard RL algorithms confirm their competitiveness against rule-based benchmarks, motivating the development of new methods to fully exploit the potential of RL to tackle the complexities of real-world tasks.