Summary of An Evolutionary Framework For Connect-4 As Test-bed For Comparison Of Advanced Minimax, Q-learning and Mcts, by Henry Taylor et al.
An Evolutionary Framework for Connect-4 as Test-Bed for Comparison of Advanced Minimax, Q-Learning and MCTS
by Henry Taylor, Leonardo Stella
First submitted to arxiv on: 26 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT); Neural and Evolutionary Computing (cs.NE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework for evaluating reinforcement learning (RL) and search algorithms in Connect-4 game domains is presented. The framework, called Evolutionary Tournament, compares the performance of three classes of algorithms: RL, Minimax, and Monte Carlo tree search (MCTS). Advanced versions of these algorithms are implemented and evaluated using various metrics, showing that MCTS achieves the best results in terms of win percentage. Minimax and Q-Learning follow as second and third place, respectively, although Q-Learning is the fastest to make a decision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists developed a new way to compare different methods for making decisions when there are many possible choices. They tested three types of algorithms: one that learns from experience (reinforcement learning), one that uses logic and analysis (Minimax), and one that looks ahead and makes predictions (Monte Carlo tree search). They found that the algorithm that looked ahead (MCTS) did best, followed by the logical one (Minimax) and then the one that learned from experience (Q-Learning). The scientists also discovered that Q-Learning was the fastest to make a decision. |
Keywords
* Artificial intelligence * Reinforcement learning
