Deep reinforcement learning in multi-agent systems: algorithms and a practical application
G Papoudakis - 2024 - era.ed.ac.uk
This thesis focuses on Reinforcement Learning (RL) in multi-agent systems. Multiagent
systems consist of several agents which in the context of this thesis, concurrently execute …
systems consist of several agents which in the context of this thesis, concurrently execute …
Agent modelling under partial observability for deep reinforcement learning
G Papoudakis, F Christianos… - Advances in Neural …, 2021 - proceedings.neurips.cc
Modelling the behaviours of other agents is essential for understanding how agents interact
and making effective decisions. Existing methods for agent modelling commonly assume …
and making effective decisions. Existing methods for agent modelling commonly assume …
Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning
This work focuses on equilibrium selection in no-conflict multi-agent games, where we
specifically study the problem of selecting a Pareto-optimal equilibrium among several …
specifically study the problem of selecting a Pareto-optimal equilibrium among several …
Benchmarking multi-agent deep reinforcement learning algorithms in cooperative tasks
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 …
evaluation tasks and criteria, making comparisons between approaches difficult. In this work …
Dealing with non-stationarity in multi-agent deep reinforcement learning
Recent developments in deep reinforcement learning are concerned with creating decision-
making agents which can perform well in various complex domains. A particular approach …
making agents which can perform well in various complex domains. A particular approach …
Deep reinforcement learning for multi-agent interaction
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 …
a given task is a core area of research in artificial intelligence and machine learning …
A review of cooperative multi-agent deep reinforcement learning
A Oroojlooy, D Hajinezhad - Applied Intelligence, 2023 - Springer
Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …
systems in recent years. The aim of this review article is to provide an overview of recent …
Deep multi-agent reinforcement learning
J Foerster - 2018 - ora.ox.ac.uk
A plethora of real world problems, such as the control of autonomous vehicles and drones,
packet delivery, and many others consists of a number of agents that need to take actions …
packet delivery, and many others consists of a number of agents that need to take actions …
Towards a standardised performance evaluation protocol for cooperative marl
R Gorsane, O Mahjoub, RJ de Kock… - Advances in …, 2022 - proceedings.neurips.cc
Multi-agent reinforcement learning (MARL) has emerged as a useful approach to solving
decentralised decision-making problems at scale. Research in the field has been growing …
decentralised decision-making problems at scale. Research in the field has been growing …
[PDF][PDF] Comparative evaluation of cooperative multi-agent deep reinforcement learning algorithms
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 …
evaluation tasks and criteria, making comparisons between approaches difficult. In this work …