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Evolving small-board Go players using coevolutionary temporal difference learning with archives

100%

Krawiec K., Jaśkowski W., Szubert M.

International Journal of Applied Mathematics and Computer Science

2011

tom 21

nr 4

717-731

We apply Coevolutionary Temporal Difference Learning (CTDL) to learn small-board Go strategies represented as weighted piece counters. CTDL is a randomized learning technique which interweaves two search processes that operate in the intra-game and inter-game mode. Intra-game learning is driven by gradient-descent Temporal Difference Learning (TDL), a reinforcement learning method that updates the board evaluation function according to differences observed between its values for consecutively visited game states. For the inter-game learning component, we provide a coevolutionary algorithm that maintains a sample of strategies and uses the outcomes of games played between them to iteratively modify the probability distribution, according to which new strategies are generated and added to the sample. We analyze CTDL's sensitivity to all important parameters, including the trace decay constant that controls the lookahead horizon of TDL, and the relative intensity of intra-game and inter-game learning. We also investigate how the presence of memory (an archive) affects the search performance, and find out that the archived approach is superior to other techniques considered here and produces strategies that outperform a handcrafted weighted piece counter strategy and simple liberty-based heuristics. This encouraging result can be potentially generalized not only to other strategy representations used for small-board Go, but also to various games and a broader class of problems, because CTDL is generic and does not rely on any problem-specific knowledge.

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The performance profile: A multi-criteria performance evaluation method for test-based problems

86%

Jaśkowski W., Liskowski P., Szubert M., Krawiec K.

International Journal of Applied Mathematics and Computer Science

2016

tom 26

nr 1

215-229

In test-based problems, solutions produced by search algorithms are typically assessed using average outcomes of interactions with multiple tests. This aggregation leads to information loss, which can render different solutions apparently indifferent and hinder comparison of search algorithms. In this paper we introduce the performance profile, a generic, domain-independent, multi-criteria performance evaluation method that mitigates this problem by characterizing the performance of a solution by a vector of outcomes of interactions with tests of various difficulty. To demonstrate the usefulness of this gauge, we employ it to analyze the behavior of Othello and Iterated Prisoner's Dilemma players produced by five (co)evolutionary algorithms as well as players known from previous publications. Performance profiles reveal interesting differences between the players, which escape the attention of the scalar performance measure of the expected utility. In particular, they allow us to observe that evolution with random sampling produces players coping well against the mediocre opponents, while the coevolutionary and temporal difference learning strategies play better against the high-grade opponents. We postulate that performance profiles improve our understanding of characteristics of search algorithms applied to arbitrary test-based problems, and can prospectively help design better methods for interactive domains.

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

2 Jaśkowski W.

2 Krawiec K.

2 Szubert M.

1 Liskowski P.

1 2016

1 2011

Wyniki wyszukiwania

Evolving small-board Go players using coevolutionary temporal difference learning with archives

The performance profile: A multi-criteria performance evaluation method for test-based problems