3 papers
cs.MA2025
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning
Lukas Schäfer, Oliver Slumbers, Stephen McAleer +3
Multi-agent reinforcement learning (MARL) requires agents to explore within a vast joint action space to find joint actions that lead to coordination. Existing value-based MARL alg…
cs.LG2024
Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated Environments
Alain Andres, Lukas Schäfer, Stefano V. Albrecht +1
One of the key challenges of Reinforcement Learning (RL) is the ability of agents to generalise their learned policy to unseen settings. Moreover, training RL agents requires large…
cs.LG2024
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…