1,313 research outputs found
Rolling Horizon Evolutionary Algorithms for General Video Game Playing
IEEE 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. However, the best results in a game are highly dependent on the specific configuration of modifications introduced over several papers, each adding additional parameters to the core algorithm. Further, the best previously published parameters have been found from only a few human-picked combinations, as the possibility space has grown beyond exhaustive search. This paper presents the state of the art in Rolling Horizon Evolutionary Algorithms, combining all modifications described in literature, as well as new ones. We then use a parameter optimiser, the N-Tuple Bandit Evolutionary Algorithm, to find the best combination of parameters in 20 games from the General Video Game AI Framework. Further, we analyse the algorithm's parameters and some interesting combinations revealed through the optimisation process. Lastly, we find new state of the art solutions on several games by automatically exploring the large parameter space of RHEA
Statistical Tree-based Population Seeding for Rolling Horizon EAs in General Video Game Playing
Multiple Artificial Intelligence (AI) methods have been proposed over recent years to create controllers to play multiple video games of different nature and complexity without revealing the specific mechanics of each of these games to the AI methods. In recent years, Evolutionary Algorithms (EAs) employing rolling horizon mechanisms have
achieved extraordinary results in these type of problems. However, some limitations are present in Rolling Horizon EAs making it a grand challenge of AI. These limitations include the wasteful mechanism of creating a population and evolving it over a fraction of a second to propose an action to be executed by the game agent. Another limitation is to use
a scalar value (fitness value) to direct evolutionary search instead of accounting for a mechanism that informs us how a particular agent behaves during the rolling horizon simulation. In this work, we address both of these issues. We introduce the use of a statistical tree that tackles the
latter limitation. Furthermore, we tackle the former limitation by employing a mechanism that allows us to seed part of the population using Monte Carlo Tree Search, a method that has dominated multiple General
Video Game AI competitions. We show how the proposed novel mechanism, called Statistical Tree-based Population Seeding, achieves better results compared to vanilla Rolling Horizon EAs in a set of 20 games, including 10 stochastic and 10 deterministic games
Warm-Start AlphaZero Self-Play Search Enhancements
Recently, AlphaZero has achieved landmark results in deep reinforcement
learning, by providing a single self-play architecture that learned three
different games at super human level. AlphaZero is a large and complicated
system with many parameters, and success requires much compute power and
fine-tuning. Reproducing results in other games is a challenge, and many
researchers are looking for ways to improve results while reducing
computational demands. AlphaZero's design is purely based on self-play and
makes no use of labeled expert data ordomain specific enhancements; it is
designed to learn from scratch. We propose a novel approach to deal with this
cold-start problem by employing simple search enhancements at the beginning
phase of self-play training, namely Rollout, Rapid Action Value Estimate (RAVE)
and dynamically weighted combinations of these with the neural network, and
Rolling Horizon Evolutionary Algorithms (RHEA). Our experiments indicate that
most of these enhancements improve the performance of their baseline player in
three different (small) board games, with especially RAVE based variants
playing strongly
The 2016 Two-Player GVGAI Competition
This paper showcases the setting and results of the first Two-Player General Video Game AI competition, which ran in 2016 at the IEEE World Congress on Computational Intelligence and the IEEE Conference on Computational Intelligence and Games. The challenges for the general game AI agents are expanded in this track from the single-player version, looking at direct player interaction in both competitive and cooperative environments of various types and degrees of difficulty. The focus is on the agents not only handling multiple problems, but also having to account for another intelligent entity in the game, who is expected to work towards their own goals (winning the game). This other player will possibly interact with first agent in a more engaging way than the environment or any non-playing character may do. The top competition entries are analyzed in detail and the performance of all agents is compared across the four sets of games. The results validate the competition system in assessing generality, as well as showing Monte Carlo Tree Search continuing to dominate by winning the overall Championship. However, this approach is closely followed by Rolling Horizon Evolutionary Algorithms, employed by the winner of the second leg of the contest
General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms
General Video Game Playing (GVGP) aims at designing an agent that is capable
of playing multiple video games with no human intervention. 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). The framework has been
expanded into several tracks during the last few years to meet the demand of
different research directions. The agents are required either to play multiple
unknown games with or without access to game simulations, or to design new game
levels or rules. This survey paper presents the VGDL, the GVGAI framework,
existing tracks, and reviews the wide use of GVGAI framework in research,
education and competitions five years after its birth. A future plan of
framework improvements is also described.Comment: 20 pages, 1 figure, accepted by IEEE To
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