10.1k citations · 22.8k across the 27 of their papers we have counts for
6 papers · 1 filter
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…
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…
Comparison of Maximum Likelihood and GAN-based training of Real NVPs
Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria +2
We train a generator by maximum likelihood and we also train the same generator architecture by Wasserstein GAN. We then compare the generated samples, exact log-probability densit…
Recurrent Environment Simulators
Silvia Chiappa, Sébastien Racaniere, Daan Wierstra +1
Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently. We improve on previous environment simulators from high-d…
PathNet: Evolution Channels Gradient Descent in Super Neural Networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell +5
For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting…