OpenAI Gym: The Classic Benchmarking Platform That Shaped Modern Reinforcement Learning

If you’ve ever trained an AI agent to play Atari games, balance a pole, drive a car, walk like a humanoid robot, or land a lunar module, you’ve almost certainly used OpenAI Gym (now simply called Gym).

Launched in April 2016 by OpenAI, Gym quickly became the standard environment suite and benchmarking tool for reinforcement learning (RL) research. Even in 2026 — with far more advanced libraries like Gymnasium, PettingZoo, Procgen, and DM Control — the original Gym remains legendary and is still widely used, cited, and extended.

This blog post explains what Gym is, why it became so influential, how it works, and where it stands today.

What Is OpenAI Gym?

OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. It provides:

A simple, unified Python API for interacting with many different simulated environments
A collection of standard benchmark tasks (Atari, classic control, robotics, algorithmic problems)
Tools for rendering, seeding, and recording episodes

The core idea is: “Write one agent → test it on many standardized environments → compare fairly with published results.”

Gym removed the friction of environment setup so researchers could focus on algorithms.

The Famous Environments (Still Iconic in 2026)

Category	Famous Environments	Difficulty Level	Why It’s Legendary
Classic Control	CartPole-v1, MountainCar-v0, Acrobot-v1	Beginner	First environments most people ever solve
Atari	Pong, Breakout, SpaceInvaders, Seaquest	Medium–Hard	DeepMind’s DQN paper used these (2015)
Box2D Physics	LunarLander-v2, BipedalWalker-v3, CarRacing-v2	Medium	Continuous control + fun visuals
MuJoCo	Humanoid-v4, Hopper-v4, Walker2d-v4	Hard	Realistic physics, continuous action spaces
Algorithmic	Copy-v0, Reverse-v0, Repeater-v0	Medium	Tests memory & pattern learning
Toy Text	FrozenLake-v1, Taxi-v3	Beginner	Discrete states/actions, great for debugging

These tasks defined the RL benchmarks for almost a decade.

How Gym Works (Core API)

The API is deliberately simple and elegant:

Python

import gym

# 1. Create environment
env = gym.make("CartPole-v1", render_mode="human")  # or "rgb_array" for recording

# 2. Reset (start new episode)
observation, info = env.reset(seed=42)

done = False
total_reward = 0

while not done:
    # 3. Choose action (your agent’s policy goes here)
    action = env.action_space.sample()           # random action (replace with your policy)

    # 4. Step: execute action, get next state & reward
    observation, reward, terminated, truncated, info = env.step(action)
    done = terminated or truncated
    total_reward += reward

    # Optional: render
    env.render()

print(f"Episode finished with reward: {total_reward}")

env.close()

That’s the entire loop — clean, readable, and consistent across 100+ environments.

Why Gym Was Revolutionary (2016–2020)

Standardization — Before Gym, every lab used different simulators → impossible to compare papers
Ease of Use — Install with pip install gym → instant access to dozens of tasks
Open & Free — Apache 2.0 license, community contributions exploded
Rendering & Recording — Built-in video saving helped debug and share results
Seeding & Reproducibility — env.seed(), env.action_space.seed() made experiments repeatable

Gym became the common language of RL research.

Gym in 2026: The Transition to Gymnasium

In 2022, the original OpenAI Gym repository was deprecated and handed over to the community as Gymnasium (gymnasium.farama.org).

Why the change?

Better long-term maintenance (Farama Foundation)
Modern API improvements (typed returns, better error handling, updated MuJoCo bindings)
Removal of deprecated dependencies (e.g., old Box2D)
Active development (new environments, multi-agent support via PettingZoo integration)

Most papers and code since 2023 use Gymnasium, but:

The API is almost identical
pip install gymnasium → gymnasium.make()
Many older Gym environments still work via gymnasium.envs.register() or compatibility layers

Who Still Uses Gym/Gymnasium Today?

Education — Almost every RL course starts with CartPole-v1
Research — Baseline comparisons, prototyping new algorithms
Industry — Robotics simulation, game AI, autonomous driving prototyping
Competitions — OpenAI Retro contests, Unity ML-Agents, Google Research Football

Final Verdict

OpenAI Gym (and its successor Gymnasium) didn’t just provide environments — it created a shared benchmark culture that accelerated reinforcement learning progress dramatically.

If you’re learning RL in 2026, start here:

Bash

pip install gymnasium[all]  # includes classic control, Box2D, Atari, MuJoCo

Solve CartPole → LunarLander → BipedalWalker → your own custom environment. The journey that shaped modern RL begins with one line:

Python

env = gymnasium.make("CartPole-v1")

Disclaimer: This article is an educational overview of OpenAI Gym and Gymnasium based on official documentation, GitHub repositories, and historical context as of February 2026. Environment availability, API details, and dependencies can change with new releases. Always refer to gymnasium.farama.org or the original Gym GitHub for the latest installation instructions, environment list, and migration guides.

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