Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Soemers, Dennis J. N. J."'
Monte-Carlo Tree Search (MCTS) typically uses multi-armed bandit (MAB) strategies designed to minimize cumulative regret, such as UCB1, as its selection strategy. However, in the root node of the search tree, it is more sensible to minimize simple re
Externí odkaz:
http://arxiv.org/abs/2411.07171
Proximal Policy Optimization (PPO) is commonly used in Reinforcement Learning from Human Feedback to align large language models (LLMs) with downstream tasks. This paper investigates the feasibility of using PPO for direct reinforcement learning (RL)
Externí odkaz:
http://arxiv.org/abs/2410.17126
Autor:
Todd, Graham, Padula, Alexander, Stephenson, Matthew, Piette, Éric, Soemers, Dennis J. N. J., Togelius, Julian
Automatically generating novel and interesting games is a complex task. Challenges include representing game rules in a computationally workable form, searching through the large space of potential games under most such representations, and accuratel
Externí odkaz:
http://arxiv.org/abs/2407.09388
Publikováno v:
2016 IEEE Conference on Computational Intelligence and Games (CIG 2016), pp. 436-443
General Video Game Playing (GVGP) is a field of Artificial Intelligence where agents play a variety of real-time video games that are unknown in advance. This limits the use of domain-specific heuristics. Monte-Carlo Tree Search (MCTS) is a search te
Externí odkaz:
http://arxiv.org/abs/2407.03049
Arguably, for the latter part of the late 20th and early 21st centuries, games have been seen as the drosophila of AI. Games are a set of exciting testbeds, whose solutions (in terms of identifying optimal players) would lead to machines that would p
Externí odkaz:
http://arxiv.org/abs/2406.18178
Autor:
Soemers, Dennis J. N. J., Bams, Guillaume, Persoon, Max, Rietjens, Marco, Sladić, Dimitar, Stefanov, Stefan, Driessens, Kurt, Winands, Mark H. M.
Many enhancements to Monte-Carlo Tree Search (MCTS) have been proposed over almost two decades of general game playing and other artificial intelligence research. However, our ability to characterise and understand which variants work well or poorly
Externí odkaz:
http://arxiv.org/abs/2406.09242
This paper proposes a new game-search algorithm, PN-MCTS, which combines Monte-Carlo Tree Search (MCTS) and Proof-Number Search (PNS). These two algorithms have been successfully applied for decision making in a range of domains. We define three area
Externí odkaz:
http://arxiv.org/abs/2303.09449
This paper presents a general approach for measuring distances between board games within the Ludii general game system. These distances are calculated using a previously published set of general board game concepts, each of which represents a common
Externí odkaz:
http://arxiv.org/abs/2301.03913
Proof-Number Search (PNS) and Monte-Carlo Tree Search (MCTS) have been successfully applied for decision making in a range of games. This paper proposes a new approach called PN-MCTS that combines these two tree-search methods by incorporating the co
Externí odkaz:
http://arxiv.org/abs/2206.03965
There are several different game description languages (GDLs), each intended to allow wide ranges of arbitrary games (i.e., general games) to be described in a single higher-level language than general-purpose programming languages. Games described i
Externí odkaz:
http://arxiv.org/abs/2205.00451