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.
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.
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.
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.
no code implementations • 22 Dec 2022 • Aleksandar Krnjaic, Raul D. Steleac, Jonathan D. Thomas, Georgios Papoudakis, Lukas Schäfer, Andrew Wing Keung To, Kuan-Ho Lao, Murat Cubuktepe, Matthew Haley, Peter Börsting, Stefano V. Albrecht
We envision a warehouse in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse.
no code implementations • 22 Dec 2023 • Filippos Christianos, Georgios Papoudakis, Matthieu Zimmer, Thomas Coste, Zhihao Wu, Jingxuan Chen, Khyati Khandelwal, James Doran, Xidong Feng, Jiacheng Liu, Zheng Xiong, Yicheng Luo, Jianye Hao, Kun Shao, Haitham Bou-Ammar, Jun Wang
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
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
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.
8 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.
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.
1 code implementation • 28 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