The Game of Tetris in Machine Learning

05/05/2019
by   Simón Algorta, et al.
0

The game of Tetris is an important benchmark for research in artificial intelligence and machine learning. This paper provides a historical account of the algorithmic developments in Tetris and discusses open challenges. Handcrafted controllers, genetic algorithms, and reinforcement learning have all contributed to good solutions. However, existing solutions fall far short of what can be achieved by expert players playing without time pressure. Further study of the game has the potential to contribute to important areas of research, including feature discovery, autonomous learning of action hierarchies, and sample-efficient reinforcement learning.

READ FULL TEXT
research
08/15/2019

Playing a Strategy Game with Knowledge-Based Reinforcement Learning

This paper presents Knowledge-Based Reinforcement Learning (KB-RL) as a ...
research
08/02/2022

Deep Reinforcement Learning for Multi-Agent Interaction

The development of autonomous agents which can interact with other agent...
research
10/16/2018

At Human Speed: Deep Reinforcement Learning with Action Delay

There has been a recent explosion in the capabilities of game-playing ar...
research
02/11/2022

The Shapley Value in Machine Learning

Over the last few years, the Shapley value, a solution concept from coop...
research
06/22/2020

Game Theory on the Ground: The Effect of Increased Patrols on Deterring Poachers

Applications of artificial intelligence for wildlife protection have foc...
research
10/20/2021

Playing 2048 With Reinforcement Learning

The game of 2048 is a highly addictive game. It is easy to learn the gam...
research
12/29/2019

Computational model discovery with reinforcement learning

The motivation of this study is to leverage recent breakthroughs in arti...

Please sign up or login with your details

Forgot password? Click here to reset