GitHub - NicoleFaye/PokemonPinballRL: Pokemon Pinball Reinforcement Learning (original) (raw)

This project uses the Stable Baselines 3 reinforcement learning library to train an agent to play Pokemon Pinball using the Pokemon Pinball Gym environment.

Example RL agent Gameplay

Overview

Pokemon Pinball RL uses deep reinforcement learning to play Pokemon Pinball. The system:

1. Uses the Pokemon Pinball Gym environment which provides a Gymnasium-compatible interface
2. Uses PyBoy to emulate the Game Boy environment under the hood
3. Uses Stable Baselines 3 for efficient agent training with vectorized environments
4. Supports various reward shaping strategies to guide learning

Installation

Prerequisites

• Python 3.8+
• ROM file for Pokemon Pinball (not included)
• Pokemon Pinball Gym environment

Install Dependencies

Clone this repository

git clone https://github.com/your-username/pokemon-pinball-rl.git cd pokemon-pinball-rl

Install requirements

pip install -r requirements.txt

Install the Pokemon Pinball Gym environment

pip install git+https://github.com/NicoleFaye/pokemon-pinball-gym.git

Usage

Training

To train an agent on Pokemon Pinball:

python train.py [--timesteps TIMESTEPS] [--num-cpu NUM_CPU] [--reward-mode REWARD_MODE]

Key parameters:

• --timesteps: Number of timesteps to train for (default: 10,000,000)
• --num-cpu: Number of parallel environments (default: 24)
• --reward-mode: Reward shaping mode - basic, catch_focused, or comprehensive (default: basic)
• --headless: Run in headless mode without visualization
• --debug: Enable debug mode
• --no-wandb: Disable WandB logging

Resuming Training

To resume training from a checkpoint:

python train.py --resume runs/basic_20250424_145544/poke_1 --timesteps 5000000

Resume parameters:

• --resume: Path to checkpoint file

When you provide a checkpoint path in the format runs/SESSION_ID/checkpoint_name, the training will automatically continue in the same session directory. Otherwise, it will create a new session directory with "resumed" in the name.

You can combine the resume parameter with any other parameters like --timesteps, --num-cpu, or --reward-mode. The resumed training will use the new parameters provided.

Benchmarking

To benchmark training performance with different numbers of parallel environments:

python bench.py [--iterations 3] [--timesteps 50000]

For more options:

Training Strategies

The project implements different reward shaping strategies through the Pokemon Pinball Gym environment:

1. Basic: Simple reward based on score difference
2. Catch-focused: Emphasizes catching Pokemon with large rewards
3. Comprehensive: Balanced approach with rewards for multiple game objectives:
   • Scoring points
   • Catching Pokemon
   • Evolving Pokemon
   • Completing stages
   • Ball upgrades
   • Survival time

Acknowledgments

Thank you to the following projects for either their library usage or for reference in the creation of this repo:

• Pokemon Pinball Gym: Gymnasium environment for Pokemon Pinball
• PyBoy: Game Boy emulator
• Pokemon Red Experiments: Pokemon Red RL project using pyboy
• Pret Pokemon Pinball Disassembly: Disassembly of Pokemon pinball for GBC
• Stable Baselines 3: Reinforcement learning library
• Gymnasium: Reinforcement learning environment interface
• WandB: Experiment tracking