4 citations · 8 across the 7 of their papers we have counts for
7 papers
Multiplayer Interactive World Models with Representation Autoencoders
Anthony Hu, Václav Volhejn, Adrien Ramanana Rahary +24
We introduce the first multiplayer world model for highly dynamic environments governed by complex physical interactions. Whereas single-player world models treat the other agents…
Object-Centric World Models from Few-Shot Annotations for Sample-Efficient Reinforcement Learning
Weipu Zhang, Adam Jelley, Trevor McInroe +2
While deep reinforcement learning (RL) from pixels has achieved remarkable success, its sample inefficiency remains a critical limitation for real-world applications. Model-based R…
Efficient Offline Reinforcement Learning: First Imitate, then Improve
Adam Jelley, Trevor McInroe, Sam Devlin +1
Supervised imitation-based approaches are often favored over off-policy reinforcement learning approaches for learning policies offline, since their straightforward optimization ob…
Aligning Agents like Large Language Models
Adam Jelley, Yuhan Cao, Dave Bignell +3
Training agents to act competently in complex 3D environments from high-dimensional visual information is challenging. Reinforcement learning is conventionally used to train such a…
Diffusion for World Modeling: Visual Details Matter in Atari
Eloi Alonso, Adam Jelley, Vincent Micheli +4
World models constitute a promising approach for training reinforcement learning agents in a safe and sample-efficient manner. Recent world models predominantly operate on sequence…
LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots
Dongge Han, Trevor McInroe, Adam Jelley +3
Large language models (LLMs) have shown significant potential for robotics applications, particularly task planning, by harnessing their language comprehension and text generation…
Contrastive Meta-Learning for Partially Observable Few-Shot Learning
Adam Jelley, Amos Storkey, Antreas Antoniou +1
Many contrastive and meta-learning approaches learn representations by identifying common features in multiple views. However, the formalism for these approaches generally assumes…