Gym4ReaL

Gymnasium-based benchmarking suite for testing RL algorithms on real-world scenarios

As research moves toward deploying Reinforcement Learning in real-world applications, this field faces a new set of challenges, which are often underexplored in current benchmarks, which tend to focus on idealized, fully observable, and stationary environments.

We present 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 to foster the development of RL to fully exploit its potential on real-world scenarios.

View it on GitHub

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Coverage of Characteristics and RL Paradigms

Env	Characteristics						RL Paradigms
	Cont. States	Cont. Actions	Part. Obs.	Part. Ctrl.	Non-Stat.	Visual In.	Freq. Adapt.	Hier. RL	Risk-Av.	Imitation	Prov. Eff.	Multi-Obj.
Dam	✅	✅		✅						✅		✅
Elevator				✅							✅
Microgrid	✅	✅		✅			✅					✅
RoboFeeder	✅	✅				✅		✅
Trading	✅		✅	✅	✅		✅		✅
WDS	✅			✅						✅		✅

Gym4ReaL is released under Apache-2.0 license.