22 citations · 76 across the 14 of their papers we have counts for
14 papers
Augmentations for Robust and Efficient Imitation Learning in Streamed Video Games
Somjit Nath, Abdelhak Lemkhenter, Pallavi Choudhury +4
Imitation learning is an appealing way to scale game-playing agents to complex 3D environments by training policies to map visual observations to actions from human demonstrations.…
Approximability of the Containment Problem for Zonotopes and Ellipsotopes
Adrian Kulmburg, Lukas Schäfer, Matthias Althoff
The zonotope containment problem, i.e., whether one zonotope is contained in another, is a central problem in control theory. Applications include detecting faults and robustifying…
Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games
Lukas Schäfer, Logan Jones, Anssi Kanervisto +7
Video games have served as useful benchmarks for the decision-making community, but going beyond Atari games towards modern games has been prohibitively expensive for the vast majo…
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…
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…
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…
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…
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning
Lukas Schäfer, Filippos Christianos, Amos Storkey +1
Successful deployment of multi-agent reinforcement learning often requires agents to adapt their behaviour. In this work, we discuss the problem of teamwork adaptation in which a t…
Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning
Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Learning control from pixels is difficult for reinforcement learning (RL) agents because representation learning and policy learning are intertwined. Previous approaches remedy thi…
Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking
Hanna Krasowski, Jakob Thumm, Marlon Müller +3
Ensuring the safety of reinforcement learning (RL) algorithms is crucial to unlock their potential for many real-world tasks. However, vanilla RL and most safe RL approaches do not…
Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Deep reinforcement learning (RL) agents that exist in high-dimensional state spaces, such as those composed of images, have interconnected learning burdens. Agents must learn an ac…
Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration
Lukas Schäfer, Filippos Christianos, Josiah P. Hanna +1
Intrinsic rewards can improve exploration in reinforcement learning, but the exploration process may suffer from instability caused by non-stationary reward shaping and strong depe…
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…
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht
Exploration in multi-agent reinforcement learning is a challenging problem, especially in environments with sparse rewards. We propose a general method for efficient exploration by…