Search Results for author:
Found 13 papers, 8 papers with code
PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games
To bridge this gap, we introduce PyTAG, a Python API for interacting with the Tabletop Games framework (TAG).
Design and Implementation of TAG: A Tabletop Games Framework
This document describes the design and implementation of the Tabletop Games framework (TAG), a Java-based benchmark for developing modern board games for AI research.
Rolling Horizon NEAT for General Video Game Playing
This paper presents a new Statistical Forward Planning (SFP) method, Rolling Horizon NeuroEvolution of Augmenting Topologies (rhNEAT).
Rolling Horizon Evolutionary Algorithms for General Video Game Playing
Game-playing Evolutionary Algorithms, specifically Rolling Horizon Evolutionary Algorithms, have recently managed to beat the state of the art in win rate across many video games.
Learning Local Forward Models on Unforgiving Games
This paper examines learning approaches for forward models based on local cell transition functions.
Project Thyia: A Forever Gameplayer
Similarly, AI game-players are run once on a game (or maybe for longer periods of time, in the case of learning algorithms which need some, still limited, period for training), and they cease to exist once the game ends.
"Did You Hear That?" Learning to Play Video Games from Audio Cues
Game-playing AI research has focused for a long time on learning to play video games from visual input or symbolic information.
A Local Approach to Forward Model Learning: Results on the Game of Life Game
This paper investigates the effect of learning a forward model on the performance of a statistical forward planning agent.
The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition
Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and different opponent types.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best
This paper introduces a simple and fast variant of Planet Wars as a test-bed for statistical planning based Game AI agents, and for noisy hyper-parameter optimisation.
General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms
In 2014, The General Video Game AI (GVGAI) competition framework was created and released with the purpose of providing researchers a common open-source and easy to use platform for testing their AI methods with potentially infinity of games created using Video Game Description Language (VGDL).
Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing
Monte Carlo Tree Search techniques have generally dominated General Video Game Playing, but recent research has started looking at Evolutionary Algorithms and their potential at matching Tree Search level of play or even outperforming these methods.
The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement
This paper describes a new evolutionary algorithm that is especially well suited to AI-Assisted Game Design.
