17 citations · 47 across the 10 of their papers we have counts for
24 papers
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…
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…
Task Relabelling for Multi-task Transfer using Successor Features
Martin Balla, Diego Perez-Liebana
Deep Reinforcement Learning has been very successful recently with various works on complex domains. Most works are concerned with learning a single policy that solves the target t…
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…
Action Advising with Advice Imitation in Deep Reinforcement Learning
Ercument Ilhan, Jeremy Gow, Diego Perez-Liebana
Action advising is a peer-to-peer knowledge exchange technique built on the teacher-student paradigm to alleviate the sample inefficiency problem in deep reinforcement learning. Re…
Learning on a Budget via Teacher Imitation
Ercument Ilhan, Jeremy Gow, Diego Perez-Liebana
Deep Reinforcement Learning (RL) techniques can benefit greatly from leveraging prior experience, which can be either self-generated or acquired from other entities. Action advisin…