Search Results for author: Georgios Papoudakis

Found 11 papers, 5 papers with code

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

1 code implementation28 Sep 2022 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 existing equilibria.

Multi-agent Reinforcement Learning reinforcement-learning +1

Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing

1 code implementation15 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 implementations28 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.

Decoder reinforcement-learning +1

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

8 code implementations14 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 implementations29 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 implementations11 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 implementations5 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 +3

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