MCTS/EA hybrid GVGAI players and game difficulty estimation

Horn, Hendrik; Volz, Vanessa; Peŕez-Liebana, Diego; Preuß, Mike

doi:10.1109/cig.2016.7860384

Cited by 19 publications

(13 citation statements)

References 11 publications

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“…Other different approaches to EAs have been explored in the past, such as combining them with other techniques in order to produce hybrids, and take advantage of the benefits of each algorithm [9]. For example, evolution was used during the simulation phase in a Monte Carlo Tree Search algorithm by Perez et al [19], or, for a different effect, the MCTS parameters were adjusted with evolutionary methods [14].…”

Section: Relevant Researchmentioning

confidence: 99%

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

Gaina

Liu

Lucas

et al. 2017

Applications of Evolutionary Computation

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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.

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Section: Relevant Researchmentioning

confidence: 99%

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

Gaina

Liu

Lucas

et al. 2017

Applications of Evolutionary Computation

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“…MCTS and Evolutionary Algorithms are tried on a number of GVGAI games in [46]. The authors fit a linear model to the games to quantify which attributes of games cause specific algorithms to succeed or fail.…”

Section: A Intrinsic Propertiesmentioning

confidence: 99%

Visualizing Multiplayer Game Spaces

2022

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We compare four different 'game-spaces' in terms of their usefulness in characterising multi-player tabletop games, with a particular interest in any underlying change to a game's characteristics as the number of players changes. In each case we take a 16-dimensional feature space, and reduce it to a 2dimensional visualizable landscape.We find that a space obtained from optimization of parameters in Monte Carlo Tree Search (MCTS) is the most directly interpretable to characterise our set of games in terms of the relative importance of imperfect information, adversarial opponents and reward sparsity. These results do not correlate with a space defined using attributes of the game-tree.This dimensionality reduction does not show any general effect as the number of players. We therefore consider the question using the original features to classify the games into two sets; those for which the characteristics of the game changes significantly as the number of players changes, and those for which there is no such effect.

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“…There have been several enhancements, strategies and hybrid formulations proposed in the RHE literature (Horn et al 2016;Gaina, Lucas, and Perez-Liebana 2017a,b). With the exception of the shift-buffer, which is explained below, we try to maintain the underlying planning mechanics as essential as possible and consider a minimal evolutionary scheme based on the (1+1) random mutation hill climber (Lucas et al 2019b).…”

Section: Rolling Horizon Within Ssmsmentioning

confidence: 99%

Predictive Control Using Learned State Space Models via Rolling Horizon Evolution

Ovalle,

Lucas

2021

Preprint

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A large part of the interest in model-based reinforcement learning derives from the potential utility to acquire a forward model capable of strategic long term decision making. Assuming that an agent succeeds in learning a useful predictive model, it still requires a mechanism to harness it to generate and select among competing simulated plans. In this paper, we explore this theme combining evolutionary algorithmic planning techniques with models learned via deep learning and variational inference. We demonstrate the approach with an agent that reliably performs online planning in a set of visual navigation tasks.

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MCTS/EA hybrid GVGAI players and game difficulty estimation

Cited by 19 publications

References 11 publications

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

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

Visualizing Multiplayer Game Spaces

Predictive Control Using Learned State Space Models via Rolling Horizon Evolution

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