I need to build an AI that can learn to play games using machine learning for my project. Instead of using pre-made environments, I want to make my own custom setup with OpenAI Gym.
Can someone explain how to build a brand new environment from scratch? I’m looking for step-by-step guidance on this.
Also wondering if there are other frameworks or methods to train AI agents for gaming besides OpenAI Gym? What are my other options for this kind of project?
Unity ML-Agents is my go-to for game AI lately. The Unity integration makes prototyping way faster than building from scratch. You get visual debugging and can tweak parameters live during training.
For custom environments, start with a simple grid-world setup. It’ll help you grasp the basics before jumping into complex game mechanics. Your reward function is crucial - I wasted weeks debugging an agent that gamed poorly designed rewards instead of actually playing correctly.
Also check out PyBullet for physics-based games. Good docs and the community’s been helpful when I hit collision detection issues in my racing project.
I’ve been building custom environments for two years now. OpenAI Gym’s docs are overwhelming at first - totally get that. Start simple: inherit from gym.Env and implement step(), reset(), render(), and close(). Don’t mess up observation_space and action_space - your RL algorithm needs these to know what it’s working with. Ray RLlib works better for distributed training and has solid debugging tools. Scales nicely when you’re running multiple environments. DeepMind’s dm_env is more flexible but harder to learn. Pro tip: test your environment like crazy before training anything on it. I wasted days thinking my agent wasn’t learning when my environment was just returning inconsistent states.
if ur just starting, try out pygame with stable-baselines3. it’s def easier than OpenAI Gym. set up a game loop, define ur action/observation spaces, then manage rewards. for multi-agent stuff, PettingZoo is worth a look!
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