Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning

2025-06-30

AI Summary

Existing RL benchmarks predominantly rely on idealized, fully observable, and stationary simulated environments, failing to capture core challenges of real-world deployment—namely, large state-action spaces, non-stationary dynamics, and partial observability. Method: We introduce the first systematic benchmark suite explicitly designed to model real-world complexity, incorporating non-stationary dynamics, constrained observation mechanisms, and high-dimensional decision spaces to construct a challenging yet representative evaluation platform. The suite supports unified training and assessment of diverse RL algorithms. Contribution/Results: Experiments demonstrate that mainstream RL algorithms exhibit substantially degraded performance on this benchmark—performing comparably to rule-based baselines—thereby validating its discriminative power. The results highlight critical limitations of current methods and reveal concrete directions for advancing RL toward practical deployment, including robustness to environmental non-stationarity, effective credit assignment under partial observability, and scalable policy optimization in high-dimensional action spaces.

Technology Category

• Machine Learning: Reinforcement Learning
• Reasoning under Uncertainty: Sequential Decision Making
• Multiagent Systems: Adversarial Agents

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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 exttt{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.

Problem

• Addressing real-world RL challenges like non-stationarity and partial observability
• Providing realistic benchmarks for RL algorithm development and evaluation
• Enhancing RL performance in complex, practical environments

Innovation

• Real-world RL benchmark suite
• Diverse tasks for practical challenges
• Standard RL vs rule-based benchmarks