Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning. - Filippos Christianos; Lukas Schäfer 001; Stefano V. Albrecht (NeurIPS, 2020)

Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms. - Georgios Papoudakis; Filippos Christianos; Lukas Schäfer 001; Stefano V. Albrecht (CoRR, 2020)

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks. - Georgios Papoudakis; Filippos Christianos; Lukas Schäfer 001; Stefano V. Albrecht (NeurIPS Datasets and Benchmarks, 2021)

Decoupling Exploration and Exploitation in Reinforcement Learning. - Lukas Schäfer 001; Filippos Christianos; Josiah Hanna; Stefano V. Albrecht (CoRR, 2021)

Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning. - Trevor McInroe; Lukas Schäfer 001; Stefano V. Albrecht (CoRR, 2021)

Robust On-Policy Data Collection for Data-Efficient Policy Evaluation. - Rujie Zhong; Josiah P. Hanna; Lukas Schäfer 001; Stefano V. Albrecht (CoRR, 2021)

Task Generalisation in Multi-Agent Reinforcement Learning. - Lukas Schäfer 001 (AAMAS, 2022)

Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration. - Lukas Schäfer 001; Filippos Christianos; Josiah P. Hanna; Stefano V. Albrecht (AAMAS, 2022)

Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning. - Rujie Zhong; Duohan Zhang; Lukas Schäfer 001; Stefano V. Albrecht; Josiah Hanna (NeurIPS, 2022)

Deep reinforcement learning for multi-agent interaction. - Ibrahim H. Ahmed 006; Cillian Brewitt; Ignacio Carlucho; Filippos Christianos; Mhairi Dunion; Elliot Fosong; Samuel Garcin; Shangmin Guo; Balint Gyevnar; Trevor McInroe; Georgios Papoudakis; Arrasy Rahman; et al. (AI Commun., 2022)

Learning Representations for Control with Hierarchical Forward Models. - Trevor McInroe; Lukas Schäfer 001; Stefano V. Albrecht (CoRR, 2022)

Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning. - Lukas Schäfer 001; Filippos Christianos; Amos J. Storkey; Stefano V. Albrecht (CoRR, 2022)

Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers. - Aleksandar Krnjaic; Jonathan D. Thomas; Georgios Papoudakis; Lukas Schäfer 001; Peter Börsting; Stefano V. Albrecht (CoRR, 2022)

Using Offline Data to Speed-up Reinforcement Learning in Procedurally Generated Environments. - Alain Andres; Lukas Schäfer 001; Esther Villar-Rodriguez; Stefano V. Albrecht; Javier Del Ser (CoRR, 2023)

Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games. - Lukas Schäfer 001; Logan Jones; Anssi Kanervisto; Yuhan Cao 001; Tabish Rashid; Raluca Georgescu; David Bignell; Siddhartha Sen 001; Andrea Treviño Gavito; Sam Devlin (CoRR, 2023)

Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers. - Aleksandar Krnjaic; Raul D. Steleac; Jonathan D. Thomas; Georgios Papoudakis; Lukas Schäfer 001; Andrew Wing-Keung To; Kuan-Ho Lao; Murat Cubuktepe; Matthew Haley; Peter Börsting; Stefano V. Albrecht (IROS, 2024)

Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning. - Trevor McInroe; Lukas Schäfer 001; Stefano V. Albrecht (Trans. Mach. Learn. Res., 2024)

Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning. - Lukas Schäfer 001; Oliver Slumbers; Stephen McAleer; Yali Du 001; Stefano V. Albrecht; David Mguni (AAMAS, 2025)

Using offline data to speed up Reinforcement Learning in procedurally generated environments. - Alain Andres; Lukas Schäfer 001; Stefano V. Albrecht; Javier Del Ser (Neurocomputing, 2025)