125 citations · 183 across the 14 of their papers we have counts for
14 papers
ViMo: A Generative Visual GUI World Model for App Agents
Dezhao Luo, Bohan Tang, Kang Li +6
App agents, which autonomously operate mobile Apps through Graphical User Interfaces (GUIs), have gained significant interest in real-world applications. Yet, they often struggle w…
AppVLM: A Lightweight Vision Language Model for Online App Control
Georgios Papoudakis, Thomas Coste, Zhihao Wu +3
The utilisation of foundation models as smartphone assistants, termed app agents, is a critical research challenge. These agents aim to execute human instructions on smartphones by…
Lightweight Neural App Control
Filippos Christianos, Georgios Papoudakis, Thomas Coste +3
This paper introduces a novel mobile phone control architecture, Lightweight Multi-modal App Control (LiMAC), for efficient interactions and control across various Android apps. Li…
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
Filippos Christianos, Georgios Papoudakis, Matthieu Zimmer +13
A key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL). However, constructing a standalone RL policy that maps perception to action directly e…
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…
Deep Reinforcement Learning for Multi-Agent Interaction
Ibrahim H. Ahmed, Cillian Brewitt, Ignacio Carlucho +14
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. Tow…
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
Filippos Christianos, Georgios Papoudakis, Arrasy Rahman +1
Sharing parameters in multi-agent deep reinforcement learning has played an essential role in allowing algorithms to scale to a large number of agents. Parameter sharing between ag…
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…
A generative model for sparse, evolving digraphs
Georgios Papoudakis, Philippe Preux, Martin Monperrus
Generating graphs that are similar to real ones is an open problem, while the similarity notion is quite elusive and hard to formalize. In this paper, we focus on sparse digraphs a…