From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI

利用图表示增强国际象棋强化学习

Enhancing Chess Reinforcement Learning with Graph Representation

针对AlphaZero依赖CNN架构泛化性差的问题，提出融入边特征的GAT图表示国际象棋强化学习架构，参数量相近时性能更优、棋力提升更快，且跨棋盘尺寸泛化性好。

Towards a Deep Reinforcement Learning Approach for Tower Line Wars

There have been numerous breakthroughs with reinforcement learning in the recent years, perhaps most notably on Deep Reinforcement Learning successfully playing and winning relatively advanced computer games. There is undoubtedly an anticipation that Deep Reinforcement Learning will play a major role when the first AI masters the complicated game plays needed to beat a professional Real-Time Strategy game player. For this to be possible, there needs to be a game environment that targets and fosters AI research, and specifically Deep Reinforcement Learning. Some game environments already exist,…

学习博弈：强化学习与游戏

Learning to Play: Reinforcement Learning and Games

本书围绕强化学习在游戏领域的研究，涵盖多种核心技术，采用Python编写示例并结合主流框架，助力读者掌握AI博弈的相关方法。

An Approach to Partial Observability in Games: Learning to Both Act and Observe

Reinforcement learning (RL) is successful at learning to play games where the entire environment is visible. However, RL approaches are challenged in complex games like Starcraft II and in real-world environments where the entire environment is not visible. In these more complex games with more limited visual information, agents must choose where to look and how to optimally use their limited visual information in order to succeed at the game. We verify that with a relatively simple model the agent can learn where to look in scenarios with a limited visual bandwidth. We develop a method for ma…

The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. As with their predecessors of chess, checkers, and backgammon, these game domains have driven research by providing sophisticated yet well-defined challenges for artificial intelligence practitioners. We continue this tradition by proposing the game of Hanabi as a new challenge do…