5 papers · 1 filter
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…
Adapting Vision-Language Models for Evaluating World Models
Mariya Hendriksen, Tabish Rashid, David Bignell +5
World models - generative models that simulate environment dynamics conditioned on past observations and actions - are gaining prominence in planning, simulation, and embodied AI.…
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…
Scaling Laws for Pre-training Agents and World Models
Tim Pearce, Tabish Rashid, Dave Bignell +3
The performance of embodied agents has been shown to improve by increasing model parameters, dataset size, and compute. This has been demonstrated in domains from robotics to video…