7 Best Alternatives to OpenAI Gym in 2026

Why look beyond OpenAI Gym

OpenAI Gym, and its successor Gymnasium, established a standard interface for reinforcement learning (RL) environments, playing a significant role in the field's development since 2016. It offers a diverse collection of environments, from classic control tasks to Atari games, facilitating algorithm development and benchmarking. However, as RL research and applications have expanded, developers and researchers often seek alternatives that provide more specialized features. This includes environments with richer physics simulations, tighter integration with specific deep learning frameworks, tools for large-scale distributed training, or advanced visualization and debugging capabilities. For instance, some projects require highly realistic 3D environments that Gym's classic tasks do not offer, while others need frameworks optimized for production-grade RL deployments rather than purely academic research. The evolving landscape of RL necessitates exploring options beyond the foundational Gym library to address specific project requirements and scale.

Top alternatives ranked

1. 1. RLlib (Ray) — Scalable reinforcement learning for production and research

   RLlib is an open-source library for reinforcement learning that runs on Ray, a distributed execution framework. It supports a range of deep RL algorithms, including PPO, IMPALA, and SAC, and can scale from a single laptop to large clusters for distributed training. RLlib is designed for both academic research and industrial applications, offering flexibility in environment integration, including Gymnasium, DeepMind Lab, and custom environments. It provides tools for experiment tracking, hyperparameter tuning, and model serving, making it suitable for end-to-end RL workflows. Its emphasis on scalability and production readiness differentiates it from more research-focused libraries.

   • Best for: Distributed RL training, production deployment of RL agents, large-scale hyperparameter tuning, and integration with existing data pipelines.

   Learn more on the RLlib documentation.

2. 2. Unity ML-Agents — Reinforcement learning in high-fidelity 3D environments

   Unity ML-Agents is an open-source toolkit that enables researchers and developers to create intelligent agents using games and simulations built with the Unity engine. It supports various RL algorithms, including PPO, SAC, and behavioral cloning, and facilitates training agents in complex 3D environments with realistic physics and graphics. ML-Agents provides a Python API for interacting with the Unity environment and includes tools for curriculum learning, imitation learning, and multi-agent scenarios. Its strength lies in its ability to leverage Unity's robust simulation capabilities for creating rich, customizable training grounds for RL agents, making it particularly useful for robotics, autonomous systems, and game AI.

   • Best for: Training agents in realistic 3D environments, robotics simulation, game AI development, multi-agent reinforcement learning, and curriculum learning.

   Learn more on the Unity ML-Agents product page.

3. 3. DeepMind's Acme — Modular and distributed reinforcement learning framework

   Acme is a research framework for reinforcement learning developed by DeepMind. It provides a collection of modular, agent-agnostic components that can be assembled to create various RL algorithms. Acme emphasizes clear separation of concerns, allowing researchers to easily swap out components like actors, learners, and adders. It supports distributed execution out of the box, building on top of Launchpad and Reverb for efficient data handling and distribution. While primarily a research tool, Acme's modular design promotes reproducibility and facilitates the development of new, complex RL algorithms, making it a strong choice for advanced RL research.

   • Best for: Advanced RL research, developing novel RL algorithms, modular experimentation with RL components, and distributed RL system design.

   Learn more on the DeepMind Acme GitHub repository.

4. 4. Stable Baselines3 — Reliable implementations of deep reinforcement learning algorithms

   Stable Baselines3 (SB3) is a set of reliable implementations of deep reinforcement learning algorithms in PyTorch. It builds upon the success of its predecessor, Stable Baselines, offering a user-friendly and well-documented codebase for popular algorithms like A2C, PPO, DQN, and SAC. SB3 provides a high-level API that simplifies the process of training and evaluating RL agents, making it accessible for both beginners and experienced practitioners. It integrates seamlessly with Gymnasium environments and offers features such as callbacks, logging, and evaluation utilities. SB3's focus on robust, tested implementations makes it a practical choice for applied RL projects and educational purposes.

   • Best for: Rapid prototyping of RL solutions, educational purposes, benchmarking existing algorithms, and applied RL projects requiring stable implementations.

   Learn more on the Stable Baselines3 documentation.

5. 5. MiniGrid — Minimalist grid-world environments for fast experimentation

   MiniGrid is a lightweight, fully observable grid-world environment suite designed for reinforcement learning research. It provides a simple, customizable, and fast environment for developing and testing RL agents, particularly those focused on partial observability, exploration, and memory. The environments are represented as compact NumPy arrays, making them computationally efficient. MiniGrid allows for easy creation of new tasks and modifications to existing ones, offering a high degree of control over environment complexity. Its minimalist design and speed make it ideal for quick iterative experimentation and for research into fundamental RL challenges without the overhead of complex simulations.

   • Best for: Research on exploration, partial observability, and memory in RL; rapid prototyping; educational demonstrations; and developing interpretable RL agents.

   Learn more on the MiniGrid GitHub repository.

6. 6. DeepMind Control Suite — High-fidelity physics environments for general motor control

   The DeepMind Control Suite (DM Control) is a collection of continuous control tasks and a physics engine (MuJoCo) for reinforcement learning research. It offers a variety of challenging environments, focusing on problems related to general motor control, such as locomotion, manipulation, and balancing. DM Control provides high-fidelity simulations with realistic physics, making it suitable for developing robust and adaptable control policies. While it has a steeper learning curve than some other environments due to its reliance on MuJoCo, it provides fine-grained control over observations and rewards, allowing for detailed experimentation in continuous action spaces. DM Control is often used in conjunction with other RL frameworks for agent training.

   • Best for: Research in continuous control, robotics, motor control, and developing agents for complex physical interactions.

   Learn more on the DeepMind Control Suite GitHub repository.

7. 7. OpenAI Gym with Roboschool — Robotics simulation environments for Gym

   Roboschool was an open-source robotics simulator that provided a set of physics-based environments compatible with the OpenAI Gym API. While Roboschool itself is no longer actively maintained and has largely been superseded by other simulators and environments, its concept of integrating realistic robotics simulations directly into the Gym framework remains relevant. For those specifically looking for Gym-compatible robotic environments without the overhead of more complex toolkits like Unity ML-Agents or DM Control, exploring community-maintained forks or similar Gym-compatible robotics environments that have emerged in its wake can be a viable option. It represented an early effort to bring more complex, continuous control tasks into the standardized Gym interface, focusing on articulated robots and locomotion.

   • Best for: Legacy projects requiring Gym-compatible robotics environments, or as a reference for developing custom physics-based Gym environments.

   Learn more on the OpenAI Roboschool GitHub repository.

Side-by-side

How to pick

Selecting an alternative to OpenAI Gym depends on your specific reinforcement learning project requirements. Consider the following decision points:

• For large-scale distributed training and production deployments: If your project involves training RL agents across multiple machines or integrating them into a production system, RLlib (Ray) is often the most suitable choice. Its architecture is built for scalability and robustness, supporting a wide array of algorithms and integration points.
• For simulations in realistic 3D environments, especially robotics or game AI: When high-fidelity visual and physical simulations are crucial, Unity ML-Agents provides a powerful platform. Leveraging the Unity engine, it enables the creation of complex, customizable 3D environments for training agents in scenarios like robotic manipulation or autonomous driving.
• For advanced RL research and developing novel algorithms: Researchers focused on pushing the boundaries of RL algorithms, particularly those requiring modularity and distributed experimentation, should consider DeepMind's Acme. Its component-based design facilitates rapid iteration on new algorithmic ideas.
• For quick prototyping, benchmarking, or educational purposes: If you need reliable, well-implemented versions of common RL algorithms for applied projects or learning, Stable Baselines3 offers a user-friendly API and robust implementations in PyTorch, integrating seamlessly with Gymnasium environments.
• For research on exploration, partial observability, or memory in simple environments: When the focus is on fundamental RL challenges within computationally inexpensive settings, MiniGrid provides a versatile and fast grid-world environment suite. Its simplicity allows researchers to concentrate on algorithmic innovations rather than complex simulation details.
• For high-fidelity continuous control tasks: If your work involves developing agents for complex motor control problems with detailed physics, the DeepMind Control Suite offers a challenging set of environments built on the MuJoCo physics engine. This is ideal for robotics and biomechanics research.
• For legacy Gym-compatible robotics projects: While OpenAI Gym with Roboschool is no longer actively maintained, if you are working with older codebases or specifically need a reference for creating physics-based Gym environments, its repository might still offer relevant insights. For new projects, other alternatives offer more current solutions.