Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations

TL;DR

This paper reviews Google DeepMind's reinforcement learning innovations in gaming, focusing on models like AlphaGo, AlphaGo Zero, and MuZero, highlighting their key advancements, training methods, and impact on AI development.

Contribution

It provides a comprehensive review of DeepMind's latest reinforcement learning models in strategy and Atari games, emphasizing their novel approaches and technological progress.

Findings

AlphaGo mastered Go surpassing human players

AlphaGo Zero eliminated reliance on human data

MuZero learned game dynamics without explicit rules

Abstract

Reinforcement Learning (RL) has been widely used in many applications, particularly in gaming, which serves as an excellent training ground for AI models. Google DeepMind has pioneered innovations in this field, employing reinforcement learning algorithms, including model-based, model-free, and deep Q-network approaches, to create advanced AI models such as AlphaGo, AlphaGo Zero, and MuZero. AlphaGo, the initial model, integrates supervised learning and reinforcement learning to master the game of Go, surpassing professional human players. AlphaGo Zero refines this approach by eliminating reliance on human gameplay data, instead utilizing self-play for enhanced learning efficiency. MuZero further extends these advancements by learning the underlying dynamics of game environments without explicit knowledge of the rules, achieving adaptability across various games, including complex Atari games. This paper reviews the significance of reinforcement learning applications in Atari and strategy-based games, analyzing these three models, their key innovations, training processes, challenges encountered, and improvements made. Additionally, we discuss advancements in the field of gaming, including MiniZero and multi-agent models, highlighting future directions and emerging AI models from Google DeepMind.