Tag Archives: General Videogame Playing

Evolving Game-specific UCB Alternatives for General Video Game Playing

3 May

Interesting paper presented as a talk at EvoGAMES 2017 in Amsterdam (The Netherlands) by Ivan Bravi.

Co-authored by: Ahmed Khalifa , Christoffer Holmgard , Julian Togelius

ABSTRACT:

At the core of the most popular version of the Monte Carlo Tree Search (MCTS) algorithm is the UCB1 (Upper Confidence Bound) equation. This equation decides which node to explore next, and therefore shapes the behavior of the search process. If the UCB1 equation is replaced with another equation, the behavior of the MCTS algorithm changes, which might increase its performance on certain problems (and decrease it on others). In this paper, we use genetic programming to evolve replacements to the UCB1 equation targeted at playing individual games in the General Video Game AI (GVGAI) Framework. Each equation is evolved to maximize playing strength in a single game, but is then also tested on all other games in our test set. For every game included in the experiments, we found a UCB replacement that performs significantly better than standard UCB1. Additionally, evolved UCB replacements also tend to improve performance in some GVGAI games for which they are not evolved, showing that improvements generalize across games to clusters of games with similar game mechanics or algorithmic performance. Such an evolved portfolio of UCB variations could be useful for a hyper-heuristic game-playing agent, allowing it to select the most appropriate heuristics for particular games or problems in general.

PRESENTATION:

Ivan Bravi – EvoGAMES – Evolving UCT alternatives

LINK TO THE PAPER:

https://link.springer.com/chapter/10.1007/978-3-319-55849-3_26

http://julian.togelius.com/Bravi2017Evolving.pdf

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Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing

25 Apr

Amazing paper presented as a talk at EvoGAMES 2017 in Amsterdam (The Netherlands) by Raluca D. Gaina.

Co-authored by: Jialin Liu, Simon M. Lucas, Diego Pérez-Liébana.

ABSTRACT:

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. Online or Rolling Horizon Evolution is one of the options available to evolve sequences of actions for planning in General Video Game Playing, but no research has been done up to date that explores the capabilities of the vanilla version of this algorithm in multiple games. This study aims to critically analyse the different configurations regarding population size and individual length in a set of 20 games from the General Video Game AI corpus. Distinctions are made between deterministic and stochastic games, and the implications of using superior time budgets are studied. Results show that there is scope for the use of these techniques, which in some configurations outperform Monte Carlo Tree Search, and also suggest that further research in these methods could boost their performance.

PRESENTATION:

https://rdgain.github.io/assets/pdf/EvoStarPdf.pdf

PAPER:

https://link.springer.com/chapter/10.1007/978-3-319-55849-3_28

 

Enjoy it!   😀