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Diffusion for World Modeling: Visual Details Matter in Atari
We introduce DIAMOND, an reinforcement learning agent trained in a diffusion world model.
Eloi Alonso, Adam Jelley, Vincent Micheli, Anssi Kanervisto, Amos Storkey, Tim Pearce, François Fleuret
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Diffusion for World Modeling: Visual Details Matter in Atari
Aligning Agents like Large Language Models
An investigation into training agents like Large Language Models (LLMs) by unsupervised pre-training, supervised fine-tuning, and finally reinforcement learning from human feedback (RLHF).
Adam Jelley, Yuhan Cao, Dave Bignell, Sam Devlin, Tabish Rashid
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Aligning Agents like Large Language Models
Efficient Offline Reinforcement Learning: The Critic is Critical
An approach for efficient offline reinforcement learning by first learning the behaviour policy and values with supervised learning, before improving on this policy with reinforcement learning.
Adam Jelley, Trevor McInroe, Sam Devlin, Amos Storkey
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Efficient Offline Reinforcement Learning: The Critic is Critical
Contrastive Meta-Learning for Partially Observable Few-Shot Learning
An approach for meta-learning contrastive representations under partial observability. We demonstrate this approach can be utilised by reinforcement learning agents to learn a representation of their environment.
Adam Jelley, Amos Storkey, Antreas Antoniou, Sam Devlin
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Contrastive Meta-Learning for Partially Observable Few-Shot Learning