L
Lukas Schäfer
Researcher at University of Edinburgh
Publications - 9
Citations - 98
Lukas Schäfer is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Computer science & Reinforcement learning. The author has an hindex of 3, co-authored 3 publications receiving 28 citations.
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Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
TL;DR: This work proposes a general method for efficient exploration by sharing experience amongst agents by applying experience sharing in an actor-critic framework and finds that it consistently outperforms two baselines and two state-of-the-art algorithms by learning in fewer steps and converging to higher returns.
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Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms.
TL;DR: This work evaluates and compares three different classes of MARL algorithms in a diverse range of multi-agent learning tasks and shows that algorithm performance depends strongly on environment properties and no algorithm learns efficiently across all learning tasks.
Proceedings Article
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
TL;DR: In this paper, the authors proposed a shared experience actor-critic (SEAC) algorithm, which applies experience sharing in an actor critic framework to explore sparse reward multi-agent environments.
Journal ArticleDOI
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning
TL;DR: This work discusses the problem of teamwork adaptation in which a team of agents needs to adapt their policies to solve novel tasks with limited limitedtuning and proposes three MATE training paradigms: independent MATE, centralised MATES, and mixed MATE which vary in the information used for the task encoding.
Journal ArticleDOI
Scalable Computation of Robust Control Invariant Sets of Nonlinear Systems
TL;DR: In this paper , robust control invariant sets for perturbed nonlinear sampled-data systems are proposed to ensure robust constraint satisfaction for an infinite time horizon.