极大的痛苦
发表于 2025-3-28 17:54:45
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gusher
发表于 2025-3-28 21:56:50
Taxonomy Matching Using Background Knowledgeystem trains a Q-network capable of strong play with no search. After two weeks of Q-learning, NeuroHex achieves respective win-rates of 20.4% as first player and 2.1% as second player against a 1-s/move version of MoHex, the current ICGA Olympiad Hex champion. Our data suggests further improvement might be possible with more training time.
孤僻
发表于 2025-3-29 00:32:26
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PANIC
发表于 2025-3-29 03:28:24
Learning from the Memory of Atari 2600posed in [.] and received comparable results in all considered games. Quite surprisingly, in the case of Seaquest we were able to train RAM-only agents which behave better than the benchmark screen-only agent. Mixing screen and RAM did not lead to an improved performance comparing to screen-only and RAM-only agents.
multiply
发表于 2025-3-29 10:55:09
Clustering-Based Online Player Modelinglay tendencies. The models can then be used to play the game or for analysis to identify how different players react to separate aspects of game states. The method is demonstrated on a tablet-based trajectory generation game called ..
亲密
发表于 2025-3-29 15:10:03
A General Approach of Game Description Decomposition for General Game Playingse serial games composed of two subgames and games with compound moves while avoiding, unlike previous works, to rely on syntactic elements that can be eliminated by simply rewriting the GDL rules. We tested our program on 40 games, compound or not, and we can decompose 32 of them successfully in less than 5 s.
拾落穗
发表于 2025-3-29 15:49:14
1865-0929 Workshop, CGW 2016, and the 5th Workshop on General Intelligence in Game-Playing Agents, GIGA 2016, held in conjunction with the 25th International Conference on Artificial Intelligence, IJCAI 2016, in New York, USA, in July 2016.The 12 revised full papers presented were carefully reviewed and selec
overbearing
发表于 2025-3-29 21:02:53
Matching Evaluations and Datasetsyout policy online that dynamically adapts the playouts to the problem at hand. We propose to enhance NRPA using more selectivity in the playouts. The idea is applied to three different problems: Bus regulation, SameGame and Weak Schur numbers. We improve on standard NRPA for all three problems.