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pro vyhledávání: '"Arjonilla, Jérôme"'
With the aim of improving performance in Markov Decision Problem in an Off-Policy setting, we suggest taking inspiration from what is done in Offline Reinforcement Learning (RL). In Offline RL, it is a common practice during policy learning to mainta
Externí odkaz:
http://arxiv.org/abs/2408.10113
Imperfect information games, such as Bridge and Skat, present challenges due to state-space explosion and hidden information, posing formidable obstacles for search algorithms. Determinization-based algorithms offer a resolution by sampling hidden in
Externí odkaz:
http://arxiv.org/abs/2408.02380
Publikováno v:
2023 IEEE Conference on Games (CoG)
In imperfect information games (e.g. Bridge, Skat, Poker), one of the fundamental considerations is to infer the missing information while at the same time avoiding the disclosure of private information. Disregarding the issue of protecting private i
Externí odkaz:
http://arxiv.org/abs/2405.14346
Publikováno v:
International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2023, 753--764
In recent years, much progress has been made in computer Go and most of the results have been obtained thanks to search algorithms (Monte Carlo Tree Search) and Deep Reinforcement Learning (DRL). In this paper, we propose to use and analyze the lates
Externí odkaz:
http://arxiv.org/abs/2405.14265
Motivated by the success of transformers in various fields, such as language understanding and image analysis, this investigation explores their application in the context of the game of Go. In particular, our study focuses on the analysis of the Tra
Externí odkaz:
http://arxiv.org/abs/2309.12675