The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments

International audienceTHE AUTHORS ARE EXTREMELY GRATEFUL TO GRID5000 for helping in designing and experimenting around Monte-Carlo Tree Search. In order to promote computer Go and stimulate further development and research in the field, the event activities, "Computational Intelligence Forum" and "World 99 Computer Go Championship," were held in Taiwan. This study focuses on the invited games played in the tournament, "Taiwanese Go players versus the computer program MoGo," held at National University of Tainan (NUTN). Several Taiwanese Go players, including one 9-Dan professional Go player and eight amateur Go players, were invited by NUTN to play against MoGo from August 26 to October 4, 2008. The MoGo program combines All Moves As First (AMAF)/Rapid Action Value Estimation (RAVE) values, online "UCT-like" values, offline values extracted from databases, and expert rules. Additionally, four properties of MoGo are analyzed including: (1) the weakness in corners, (2) the scaling over time, (3) the behavior in handicap games, and (4) the main strength of MoGo in contact fights. The results reveal that MoGo can reach the level of 3 Dan with, (1) good skills for fights, (2) weaknesses in corners, in particular for "semeai" situations, and (3) weaknesses in favorable situations such as handicap games. It is hoped that the advances in artificial intelligence and computational power will enable considerable progress in the field of computer Go, with the aim of achieving the same levels as computer chess or Chinese chess in the future

A Multi-Agent Approach to the Game of Go Using Genetic Algorithms

This is the published version. Copyright De GruyterMany researchers have written or attempted to write programs that play the ancient Chinese board game called Go. Although some programs play the game quite well compared with beginners, few play extremely well, and none of the best programs rely on soft computing artificial intelligence techniques like genetic algorithms or neural networks. This paper explores the advantages and possibilities of using genetic algorithms to evolve a multiagent Go player. We show that although individual agents may play poorly, collectively the agents working together play the game significantly better

Pgx: Hardware-accelerated Parallel Game Simulators for Reinforcement Learning

We propose Pgx, a suite of board game reinforcement learning (RL) environments written in JAX and optimized for GPU/TPU accelerators. By leveraging auto-vectorization and Just-In-Time (JIT) compilation of JAX, Pgx can efficiently scale to thousands of parallel executions over accelerators. In our experiments on a DGX-A100 workstation, we discovered that Pgx can simulate RL environments 10-100x faster than existing Python RL libraries. Pgx includes RL environments commonly used as benchmarks in RL research, such as backgammon, chess, shogi, and Go. Additionally, Pgx offers miniature game sets and baseline models to facilitate rapid research cycles. We demonstrate the efficient training of the Gumbel AlphaZero algorithm with Pgx environments. Overall, Pgx provides high-performance environment simulators for researchers to accelerate their RL experiments. Pgx is available at https://github.com/sotetsuk/pgx.Comment: 9 page

Planning and Policy Improvement

MuZero is currently the most successful general reinforcement learning algorithm, achieving the state of the art on Go, chess, shogi, and Atari. We want to help MuZero to be successful in even more domains. Towards that, we do three steps: 1) We identify MuZero's problems on stochastic environments and provide ways to model enough information to support causally correct planning. 2) We develop a strong baseline agent on Atari. This agent, named Muesli, matches the state of the art on Atari, even without deep search. The conducted ablations inform us about the importance of model learning, deep search, large networks, and regularized policy optimization. 3) Because MuZero's tree search is very helpful on Go and chess, we use the principle of policy improvement to design search algorithms with even better properties. The new algorithms, named Gumbel AlphaZero and Gumbel MuZero, match the state of the art on Go, chess, and Atari, and significantly improve prior performance when planning with few simulations

Learning to Search in Reinforcement Learning

In this thesis, we investigate the use of search based algorithms with deep neural networks to tackle a wide range of problems ranging from board games to video games and beyond. Drawing inspiration from AlphaGo, the first computer program to achieve superhuman performance in the game of Go, we developed a new algorithm AlphaZero. AlphaZero is a general reinforcement learning algorithm that combines deep neural networks with a Monte Carlo Tree search for planning and learning. Starting completely from scratch, without any prior human knowledge beyond the basic rules of the game, AlphaZero managed to achieve superhuman performance in Go, chess and shogi. Subsequently, building upon the success of AlphaZero, we investigated ways to extend our methods to problems in which the rules are not known or cannot be hand-coded. This line of work led to the development of MuZero, a model-based reinforcement learning agent that builds a deterministic internal model of the world and uses it to construct plans in its imagination. We applied our method to Go, chess, shogi and the classic Atari suite of video-games, achieving superhuman performance. MuZero is the first RL algorithm to master a variety of both canonical challenges for high performance planning and visually complex problems using the same principles. Finally, we describe Stochastic MuZero, a general agent that extends the applicability of MuZero to highly stochastic environments. We show that our method achieves superhuman performance in stochastic domains such as backgammon and the classic game of 2048 while matching the performance of MuZero in deterministic ones like Go

Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players

The objective of this thesis is model some processes from the nature as evolution and co-evolution, and proposing some techniques that can ensure that these learning process really happens and useful to solve some complex problems as Go game. The Go game is ancient and very complex game with simple rules which still is a challenge for the Artificial Intelligence. This dissertation cover some approaches that were applied to solve this problem, proposing solve this problem using competitive and cooperative co-evolutionary learning methods and other techniques proposed by the author. To study, implement and prove these methods were used some neural networks structures, a framework free available and coded many programs. The techniques proposed were coded by the author, performed many experiments to find the best configuration to ensure that co-evolution is progressing and discussed the results. Using co-evolutionary learning processes can be observed some pathologies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, cycling dynamics and forgetting. According to some authors, one solution to solve these co-evolution pathologies is introduce more diversity in populations that are evolving. In this thesis is proposed some techniques to introduce more diversity and some diversity measurements for neural networks structures to monitor diversity during co-evolution. The genotype diversity evolved were analyzed in terms of its impact to global fitness of the strategies evolved and their generalization. Additionally, it was introduced a memory mechanism in the network neural structures to reinforce some strategies in the genes of the neurons evolved with the intention that some good strategies learned are not forgotten. In this dissertation is presented some works from other authors in which cooperative and competitive co-evolution has been applied. The Go board size used in this thesis was 9x9, but can be easily escalated to more bigger boards.The author believe that programs coded and techniques introduced in this dissertation can be used for other domains