Best OpenAI Gym Alternatives ranked by AI · updated Aug 2026

OpenAI Gym is an open-source Python toolkit for developing and comparing reinforcement learning algorithms through standardized environments. It was widely used by researchers and developers, but the project is no longer actively maintained and has been superseded by Gymnasium.

Developer: OpenAI Price: Free 🎯 github.com/openai/gym

🔓 Open Source 🎮 Simulation Python reinforcement learning environments

Top 6 OpenAI Gym alternatives

Gymnasium

Farama Foundation

💡 Pick it for a maintained, drop-in successor with the closest API and ecosystem to OpenAI Gym.

Gymnasium is the maintained successor to OpenAI Gym, providing a standard Python API and a broad collection of reinforcement learning environments. It...

Pros

Maintained successor with broad compatibility with current reinforcement learning libraries
Preserves Gym's familiar environment API while improving termination and reset handling
Offers classic control, Box2D, MuJoCo, Atari, and other environment families

Cons

Some older Gym projects require API migration
Environment coverage still depends on separate packages
Does not include complete training algorithms

Free

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PettingZoo

Farama Foundation

💡 Pick it when your agents compete or cooperate rather than acting alone in a Gym-style environment.

PettingZoo is an open-source Python API and environment suite for multi-agent reinforcement learning. It supports turn-based and parallel interactions across games, classic...

Pros

Best fit for multi-agent reinforcement learning compared with Gym
Supports both sequential and parallel agent interaction models
Provides standardized testing utilities and environment conventions

Cons

More complex than Gym for single-agent experiments
Smaller single-agent environment selection
Requires multi-agent-specific algorithm support

Free

Alternatives to PettingZoo → Visit site

Unity ML-Agents

💡 Pick it for visually rich 3D and game-world simulations instead of lightweight benchmark environments.

Unity ML-Agents enables games and simulations to serve as environments for training intelligent agents.

Pros

Integration with Unity game engine
Realistic 3D environments for training

Cons

Steep learning curve for beginners
Limited support for non-game environments

Free

Alternatives to Unity ML-Agents →

Brax

Google DeepMind

💡 Pick it for high-throughput physics and robotics experiments on GPUs or TPUs.

Brax is an open-source differentiable physics engine and reinforcement learning environment system written for accelerated numerical computing. It is designed for researchers...

Pros

Can massively parallelize physics simulation on accelerators
Well suited to locomotion, control, and differentiable simulation research
Integrates with JAX-based reinforcement learning workflows

Cons

Narrower environment scope than Gym's general-purpose catalog
Requires JAX and accelerator-oriented development knowledge
Physics fidelity and supported features vary by environment

Free

Alternatives to Brax → Visit site

TF-Agents

Google

💡 Pick it when you need complete TensorFlow training components rather than only standardized environments.

TF-Agents is an open-source reinforcement learning library offering agents, training drivers, replay buffers, metrics, and environment abstractions for TensorFlow. It is aimed...