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The YGO-Agent framework allows researchers and enthusiasts to develop AI bots that play the Yu-Gi-Oh card game using reinforcement learning. It wraps the YGOPRO game simulator into an OpenAI Gym-compatible environment, defining state representations such as hand, field, and life points, and action representations including summoning, spell/trap activation, and attacking. Rewards are based on win/loss outcomes, damage dealt, and game progress. The agent architecture uses PyTorch to implement DQN, with options for custom network architectures, experience replay, and epsilon-greedy exploration. Logging modules record training curves, win rates, and detailed move logs for analysis. The framework is modular, enabling users to replace or extend components such as the reward function or action space.

Dino Reinforcement Learning offers a comprehensive toolkit for training an AI agent to play the Chrome dinosaur game via reinforcement learning. By integrating with a headless Chrome instance through Selenium, it captures real-time game frames and processes them into state representations optimized for deep Q-network inputs. The framework includes modules for replay memory, epsilon-greedy exploration, convolutional neural network models, and training loops with customizable hyperparameters. Users can monitor training progress via console logs and save checkpoints for later evaluation. Post-training, the agent can be deployed to play live games autonomously or benchmarked against different model architectures. The modular design allows easy substitution of RL algorithms, making it a flexible platform for experimentation.