https://caiyf03.github.io/tags/control/ControlHugoBlox Kit (https://hugoblox.com)en-usFri, 26 Jan 2024 00:00:00 +0000https://caiyf03.github.io/media/icon_hu_702a800cd775dbac.pnghttps://caiyf03.github.io/tags/control/https://caiyf03.github.io/projects/heartai/Fri, 26 Jan 2024 00:00:00 +0000https://caiyf03.github.io/projects/heartai/<h2 id="team">Team</h2> <ul> <li>YiFan Cai (leader)</li> <li>JunXian Guo</li> <li>ZhiHang Wang</li> <li>etc</li> </ul> <h2 id="overview">Overview</h2> <p>This project develops an AI agent for the Hearts card game, a multi-player, imperfect-information environment requiring strategic reasoning and long-term planning. The goal is to compare classical search-based and learning-based decision-making methods under realistic game constraints.</p> <h2 id="key-features">Key Features</h2> <p>-Supports multiple AI paradigms within a unified game engine</p> <p>-Adaptive MCTS for improved runtime–performance trade-offs</p> <p>-State abstraction enabling Q-learning in large, discrete action spaces</p> <p>-Simulation and visualization tools for strategy comparison and analysis</p> <h2 id="methods">Methods</h2> <p>We implemented and evaluated greedy, Monte Carlo Tree Search (MCTS), and Q-learning agents. MCTS handles uncertainty through randomized rollouts and includes an adaptive variant that reduces computation when the agent is ahead. To enable learning in a large state space, the Q-learning agent uses state abstraction and reward shaping, learning strategic behavior through repeated self-play.</p> <h2 id="results">Results</h2> <p>Experiments over simulated games show that MCTS achieves the strongest performance at the cost of higher computation, while the Q-learning agent outperforms greedy and random baselines with improved adaptability. The results highlight trade-offs between decision quality, efficiency, and learning capability in imperfect-information games.</p>