Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers

no code implementations • 22 Dec 2022 • Aleksandar Krnjaic, Jonathan D. Thomas, Georgios Papoudakis, Lukas Schäfer, Peter Börsting, Stefano V. Albrecht

This project leverages advances in multi-agent reinforcement learning (MARL) to improve the efficiency and flexibility of order-picking systems for commercial warehouses.

Multi-agent Reinforcement Learning

Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning

no code implementations • 28 Sep 2022 • Filippos Christianos, Georgios Papoudakis, Stefano V. Albrecht

Equilibrium selection in multi-agent games refers to the problem of selecting a Pareto-optimal equilibrium.

Multi-agent Reinforcement Learning reinforcement-learning +1

Deep Reinforcement Learning for Multi-Agent Interaction

3 code implementations • 2 Aug 2022 • Ibrahim H. Ahmed, Cillian Brewitt, Ignacio Carlucho, Filippos Christianos, Mhairi Dunion, Elliot Fosong, Samuel Garcin, Shangmin Guo, Balint Gyevnar, Trevor McInroe, Georgios Papoudakis, Arrasy Rahman, Lukas Schäfer, Massimiliano Tamborski, Giuseppe Vecchio, Cheng Wang, Stefano V. Albrecht

The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning.

BIG-bench Machine Learning Causal Inference +4

Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing

1 code implementation • 15 Feb 2021 • Filippos Christianos, Georgios Papoudakis, Arrasy Rahman, Stefano V. Albrecht

Sharing parameters in multi-agent deep reinforcement learning has played an essential role in allowing algorithms to scale to a large number of agents.

Multi-agent Reinforcement Learning reinforcement-learning +1

Local Information Opponent Modelling Using Variational Autoencoders

no code implementations • 28 Sep 2020 • Georgios Papoudakis, Filippos Christianos, Stefano V Albrecht

Modelling the behaviours of other agents (opponents) is essential for understanding how agents interact and making effective decisions.

Agent Modelling under Partial Observability for Deep Reinforcement Learning

1 code implementation • NeurIPS 2021 • Georgios Papoudakis, Filippos Christianos, Stefano V. Albrecht

Existing methods for agent modelling commonly assume knowledge of the local observations and chosen actions of the modelled agents during execution.

reinforcement-learning reinforcement Learning

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

6 code implementations • 14 Jun 2020 • Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht

Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria, making comparisons between approaches difficult.

Benchmarking reinforcement-learning +1

Variational Autoencoders for Opponent Modeling in Multi-Agent Systems

no code implementations • 29 Jan 2020 • Georgios Papoudakis, Stefano V. Albrecht

Modeling the behavior of other agents (opponents) is essential in understanding the interactions of the agents in the system.

Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

no code implementations • 11 Jun 2019 • Georgios Papoudakis, Filippos Christianos, Arrasy Rahman, Stefano V. Albrecht

Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains.

Decision Making Meta-Learning +3

Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks

no code implementations • 5 Jul 2018 • Georgios Papoudakis, Kyriakos C. Chatzidimitriou, Pericles A. Mitkas

In this paper we propose a divide and conquer deep reinforcement learning solution and we test our agent in the first person shooter (FPS) game of Doom.

Atari Games Game of Doom +2