265 citations · 1.6k across the 43 of their papers we have counts for
6 papers · 1 filter
Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human--like learning
Pierre-Yves Oudeyer
Autonomous lifelong development and learning is a fundamental capability of humans, differentiating them from current deep learning systems. However, other branches of artificial i…
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017
M. Botvinick, D. G. T. Barrett, P. Battaglia +16
We agree with Lake and colleagues on their list of key ingredients for building humanlike intelligence, including the idea that model-based reasoning is essential. However, we favo…
Building machines that adapt and compute like brains
Nikolaus Kriegeskorte, Robert M. Mok
Building machines that learn and think like humans is essential not only for cognitive science, but also for computational neuroscience, whose ultimate goal is to understand how co…
Learning model-based planning from scratch
Razvan Pascanu, Yujia Li, Oriol Vinyals +7
Conventional wisdom holds that model-based planning is a powerful approach to sequential decision-making. It is often very challenging in practice, however, because while a model c…
Imagination-Augmented Agents for Deep Reinforcement Learning
Théophane Weber, Sébastien Racanière, David P. Reichert +12
We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing mo…
Visual Interaction Networks
Nicholas Watters, Andrea Tacchetti, Theophane Weber +3
From just a glance, humans can make rich predictions about the future state of a wide range of physical systems. On the other hand, modern approaches from engineering, robotics, an…