2 citations · 3 across the 7 of their papers we have counts for
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cs.LG2024★ 2 cited
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…
cs.LG2024
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…
cs.LG2024
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…