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General Intelligence Requires Rethinking Exploration
Minqi Jiang, Tim Rocktäschel, Edward Grefenstette
We are at the cusp of a transition from "learning from data" to "learning what data to learn from" as a central focus of artificial intelligence (AI) research. While the first-orde…
Learning General World Models in a Handful of Reward-Free Deployments
Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano +5
Building generally capable agents is a grand challenge for deep reinforcement learning (RL). To approach this challenge practically, we outline two key desiderata: 1) to facilitate…
The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs
Laura Ruis, Akbir Khan, Stella Biderman +3
Despite widespread use of LLMs as conversational agents, evaluations of performance fail to capture a crucial aspect of communication: interpreting language in context -- incorpora…
Improving Policy Learning via Language Dynamics Distillation
Victor Zhong, Jesse Mu, Luke Zettlemoyer +2
Recent work has shown that augmenting environments with language descriptions improves policy learning. However, for environments with complex language abstractions, learning how t…
Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning
Michael Matthews, Mikayel Samvelyan, Jack Parker-Holder +2
Practising and honing skills forms a fundamental component of how humans learn, yet artificial agents are rarely specifically trained to perform them. Instead, they are usually tra…
Efficient Planning in a Compact Latent Action Space
Zhengyao Jiang, Tianjun Zhang, Michael Janner +4
Planning-based reinforcement learning has shown strong performance in tasks in discrete and low-dimensional continuous action spaces. However, planning usually brings significant c…