8 citations · 9 across the 4 of their papers we have counts for
7 papers
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…
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment
Philip J. Ball, Cong Lu, Jack Parker-Holder +1
Reinforcement learning from large-scale offline datasets provides us with the ability to learn policies without potentially unsafe or impractical exploration. Significant progress…
OffCon: What is state of the art anyway?
Philip J. Ball, Stephen J. Roberts
Two popular approaches to model-free continuous control tasks are SAC and TD3. At first glance these approaches seem rather different; SAC aims to solve the entropy-augmented MDP b…
A Study on Efficiency in Continual Learning Inspired by Human Learning
Philip J. Ball, Yingzhen Li, Angus Lamb +1
Humans are efficient continual learning systems; we continually learn new skills from birth with finite cells and resources. Our learning is highly optimized both in terms of capac…
Ready Policy One: World Building Through Active Learning
Philip Ball, Jack Parker-Holder, Aldo Pacchiano +2
Model-Based Reinforcement Learning (MBRL) offers a promising direction for sample efficient learning, often achieving state of the art results for continuous control tasks. However…
Active inference: demystified and compared
Noor Sajid, Philip J. Ball, Thomas Parr +1
Active inference is a first principle account of how autonomous agents operate in dynamic, non-stationary environments. This problem is also considered in reinforcement learning (R…