An Introduction to Building Custom Reinforcement Learning Environments Using OpenAI Gym

Abstraction: Tutorial for building custom OpenAI Gym RL environment

Key points:

• Beginner tutorial building a custom OpenAI Gym environment: a 6x6 grid game where a blue agent moves up/down/left/right to reach a green (win, +1) square while avoiding red (lose, −1); every non-terminal move gives −0.01 to encourage short paths.
• RL fundamentals: environment + agent, with state, actions, and rewards; reward design matters (a badly chosen reward could make the agent blow itself up).
• Gym requires numeric state (grid flattened to a 36-element row via matrix flattening); observation_space as spaces.Box(0,3,[36]) and action_space as spaces.Discrete(4).
• A Gym environment class subclasses Env and implements four functions: __init__, step(action) → (state, reward, done, info), reset(), and render().
• Uses stable_baselines3 to train models without writing algorithms; check_env validates Gym compatibility. (Author Paul Swenson, 2022; code on GitHub.)

Connections: Openai Gym · Stable Baselines3 · Reinforcement Learning · RL Environments · Markov Decision Process

Source: https://medium.com/@paulswenson2/an-introduction-to-building-custom-reinforcement-learning-environment-using-openai-gym-d8a5e7cf07ea