125 citations · 183 across the 14 of their papers we have counts for
7 papers · 1 filter
Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers
Aleksandar Krnjaic, Raul D. Steleac, Jonathan D. Thomas +8
We consider a warehouse in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse. The fundamental problem we tackle, calle…
Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning
Filippos Christianos, Georgios Papoudakis, Stefano V. Albrecht
This work focuses on equilibrium selection in no-conflict multi-agent games, where we specifically study the problem of selecting a Pareto-optimal Nash equilibrium among several ex…
Agent Modelling under Partial Observability for Deep Reinforcement Learning
Georgios Papoudakis, Filippos Christianos, Stefano V. Albrecht
Modelling the behaviours of other agents is essential for understanding how agents interact and making effective decisions. Existing methods for agent modelling commonly assume kno…
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
Georgios Papoudakis, Filippos Christianos, Lukas Schäfer +1
Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria, making comparisons between approaches difficult. In this work, we…
Variational Autoencoders for Opponent Modeling in Multi-Agent Systems
Georgios Papoudakis, Stefano V. Albrecht
Multi-agent systems exhibit complex behaviors that emanate from the interactions of multiple agents in a shared environment. In this work, we are interested in controlling one agen…
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning
Georgios Papoudakis, Filippos Christianos, Arrasy Rahman +1
Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains. A particular approach which…
Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks
Georgios Papoudakis, Kyriakos C. Chatzidimitriou, Pericles A. Mitkas
Recent developments in deep reinforcement learning have enabled the creation of agents for solving a large variety of games given a visual input. These methods have been proven suc…