10.1k citations · 22.8k across the 27 of their papers we have counts for
7 papers · 1 filter
SIMA 2: A Generalist Embodied Agent for Virtual Worlds
SIMA team, Adrian Bolton, Alexander Lerchner +63
We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a signifi…
Learning to Search with MCTSnets
Arthur Guez, Théophane Weber, Ioannis Antonoglou +5
Planning problems are among the most important and well-studied problems in artificial intelligence. They are most typically solved by tree search algorithms that simulate ahead in…
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…
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…
Building Machines That Learn and Think Like People
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum +1
Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks t…
Efficient Natural Evolution Strategies
Yi Sun, Daan Wierstra, Tom Schaul +1
Efficient Natural Evolution Strategies (eNES) is a novel alternative to conventional evolutionary algorithms, using the natural gradient to adapt the mutation distribution. Unlike…