Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning

• URL: http://arxiv.org/abs/2507.00257v1
• Date: Mon, 30 Jun 2025 20:47:50 GMT
• Title: Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning
• Authors: Davide Salaorni, Vincenzo De Paola, Samuele Delpero, Giovanni Dispoto, Paolo Bonetti, Alessio Russo, Giuseppe Calcagno, Francesco Trovò, Matteo Papini, Alberto Maria Metelli, Marco Mussi, Marcello Restelli,
• Abstract summary: We introduce textttGym4ReaL, a suite of realistic environments designed to support the development and evaluation of RL algorithms.<n>Our experimental results show that, in these settings, standard RL algorithms confirm their competitiveness against rule-based benchmarks.
• Score: 46.03129508525389
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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