1 citations · 2 across the 6 of their papers we have counts for
7 papers
Strategy Game-Playing with Size-Constrained State Abstraction
Linjie Xu, Diego Perez-Liebana, Alexander Dockhorn
Playing strategy games is a challenging problem for artificial intelligence (AI). One of the major challenges is the large search space due to a diverse set of game components. In…
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning
Linjie Xu, Zichuan Liu, Alexander Dockhorn +4
One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency. Compared to single agent RL, the sample efficiency for Multi-Agent Reinforcement Learning (MA…
Mildly Constrained Evaluation Policy for Offline Reinforcement Learning
Linjie Xu, Zhengyao Jiang, Jinyu Wang +2
Offline reinforcement learning (RL) methodologies enforce constraints on the policy to adhere closely to the behavior policy, thereby stabilizing value learning and mitigating the…
Elastic Monte Carlo Tree Search with State Abstraction for Strategy Game Playing
Linjie Xu, Jorge Hurtado-Grueso, Dominic Jeurissen +2
Strategy video games challenge AI agents with their combinatorial search space caused by complex game elements. State abstraction is a popular technique that reduces the state spac…
Portfolio Search and Optimization for General Strategy Game-Playing
Alexander Dockhorn, Jorge Hurtado-Grueso, Dominik Jeurissen +2
Portfolio methods represent a simple but efficient type of action abstraction which has shown to improve the performance of search-based agents in a range of strategy games. We fir…
Generating Diverse and Competitive Play-Styles for Strategy Games
Diego Perez-Liebana, Cristina Guerrero-Romero, Alexander Dockhorn +3
Designing agents that are able to achieve different play-styles while maintaining a competitive level of play is a difficult task, especially for games for which the research commu…
Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network
Pengxin Guo, Chang Deng, Linjie Xu +2
Deep multi-task learning attracts much attention in recent years as it achieves good performance in many applications. Feature learning is important to deep multi-task learning for…