Gym4ReaL: A Benchmark Suite for Evaluating Reinforcement Learning in Realistic Domains

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

Keywords: Reinforcement Learning, Benchmarking, Real-world

TL;DR: Gym4ReaL: a benchmarking suite of realistic environments designed to support the development and evaluation of reinforcement learning algorithms in real-world domains.

Abstract: In recent years, _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 `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.

Confirmation: I understand that authors of each paper submitted to EWRL may be asked to review 2-3 other submissions to EWRL.

Serve As Reviewer: ~Davide_Salaorni1, ~Vincenzo_De_Paola3, ~Samuele_Delpero1, ~Giovanni_Dispoto1, ~Paolo_Bonetti1, ~Alessio_Russo2

Track: Regular Track: unpublished work

Submission Number: 110