Investigating Vanilla MCTS Scaling on the GVG-AI Game Corpus

The General Video Game AI Competition (GVG-AI) invites submissions of controllers to play games specified in the Video Game Description Language (VGDL), testing them against each other and several baselines. One of the baselines that has done surprisingly well in some of the competitions is sampleMCTS, a straightforward implementation of Monte Carlo tree search (MCTS). Although it has done worse in other iterations of the competition, this has produced a nagging worry to us that perhaps the GVG-AI competition might be too easy, especially since performance profiling suggests that significant increases in number of MCTS iterations that can be completed in a given time limit will be possible through optimizations to the GVG-AI competition framework. To better understand the potential performance of the baseline vanilla MCTS controller, I perform scaling experiments, running it against the 62 games in the public GVG-AI corpus as the time budget is varied from about 1/30 of that in the current competition, through around 30x the current competition's budget. I find that it does not in fact master the games even given 30x the current time budget, so the challenge of the GVG-AI competition is safe (at least against this baseline). However, I do find that given enough computational budget, it manages to avoid explicitly losing on most games, despite failing to win them and ultimately losing as time expires, suggesting an asymmetry in the current GVG-AI competition's challenge: not losing is significantly easier than winning

Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing

In this paper, we study the effects of several Monte Carlo Tree Search (MCTS) modifications for video game testing. Although MCTS modifications are highly studied in game playing, their impacts on finding bugs are blank. We focused on bug finding in our previous study where we introduced synthetic and human-like test goals and we used these test goals in Sarsa and MCTS agents to find bugs. In this study, we extend the MCTS agent with several modifications for game testing purposes. Furthermore, we present a novel tree reuse strategy. We experiment with these modifications by testing them on three testbed games, four levels each, that contain 45 bugs in total. We use the General Video Game Artificial Intelligence (GVG-AI) framework to create the testbed games and collect 427 human tester trajectories using the GVG-AI framework. We analyze the proposed modifications in three parts: we evaluate their effects on bug finding performances of agents, we measure their success under two different computational budgets, and we assess their effects on human-likeness of the human-like agent. Our results show that MCTS modifications improve the bug finding performance of the agents

Population seeding techniques for Rolling Horizon Evolution in General Video Game Playing

While Monte Carlo Tree Search and closely related methods have dominated General Video Game Playing, recent research has demonstrated the promise of Rolling Horizon Evolutionary Algorithms as an interesting alternative. However, there is little attention paid to population initialization techniques in the setting of general real-time video games. Therefore, this paper proposes the use of population seeding to improve the performance of Rolling Horizon Evolution and presents the results of two methods, One Step Look Ahead and Monte Carlo Tree Search, tested on 20 games of the General Video Game AI corpus with multiple evolution parameter values (population size and individual length). An in-depth analysis is carried out between the results of the seeding methods and the vanilla Rolling Horizon Evolution. In addition, the paper presents a comparison to a Monte Carlo Tree Search algorithm. The results are promising, with seeding able to boost performance significantly over baseline evolution and even match the high level of play obtained by the Monte Carlo Tree Search

Shallow decision-making analysis in General Video Game Playing

The General Video Game AI competitions have been the testing ground for several techniques for game playing, such as evolutionary computation techniques, tree search algorithms, hyper heuristic based or knowledge based algorithms. So far the metrics used to evaluate the performance of agents have been win ratio, game score and length of games. In this paper we provide a wider set of metrics and a comparison method for evaluating and comparing agents. The metrics and the comparison method give shallow introspection into the agent's decision making process and they can be applied to any agent regardless of its algorithmic nature. In this work, the metrics and the comparison method are used to measure the impact of the terms that compose a tree policy of an MCTS based agent, comparing with several baseline agents. The results clearly show how promising such general approach is and how it can be useful to understand the behaviour of an AI agent, in particular, how the comparison with baseline agents can help understanding the shape of the agent decision landscape. The presented metrics and comparison method represent a step toward to more descriptive ways of logging and analysing agent's behaviours

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

Self-adaptive MCTS for General Video Game Playing

Monte-carlo tree search (mcts) has shown particular success in general game playing (ggp) and general video game playing (gvgp) and many enhancements and variants have been developed. Recently, an on-line adaptive parameter tuning mechanism for mcts agents has been proposed that almost achieves the same performance as off-line tuning in ggp.in this paper we apply the same approach to gvgp and use the popular general video game ai (gvgai) framework, in which the time allowed to make a decision is only 40 ms. We design three self-adaptive mcts (sa-mcts) agents that optimize on-line the parameters of a standard non-self-adaptive mcts agent of gvgai. The three agents select the parameter values using naïve monte-carlo, an evolutionary algorithm and an n-tuple bandit evolutionary algorithm respectively, and are tested on 20 single-player games of gvgai.the sa-mcts agents achieve more robust results on the tested games. With the same time setting, they perform similarly to the baseline standard mcts agent in the games for which the baseline agent performs well, and significantly improve the win rate in the games for which the baseline agent performs poorly. As validation, we also test the performance of non-self-adaptive mcts instances that use the most sampled parameter settings during the on-line tuning of each of the three sa-mcts agents for each game. Results show that these parameter settings improve the win rate on the games wait for breakfast and escape by 4 times and 150 times, respectively