Consequently, impressive improvements in sample efficiency have been achieved when a suitable MDP homomorphism can be constructed a priori -- usually by exploiting a practioner's knowledge of environment symmetries.
Learning representations of algorithms is an emerging area of machine learning, seeking to bridge concepts from neural networks with classical algorithms.
We show that these pretrained representations drive meaningful, task-relevant exploration and improve performance on 3D simulated environments.
no code implementations • 17 Feb 2022 • Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adria Puigdomenech Badia, Arthur Guez, Mehdi Mirza, Peter C. Humphreys, Ksenia Konyushkova, Laurent SIfre, Michal Valko, Simon Osindero, Timothy Lillicrap, Nicolas Heess, Charles Blundell
In this paper we explore an alternative paradigm in which we train a network to map a dataset of past experiences to optimal behavior.
Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings.
Many reinforcement learning (RL) agents require a large amount of experience to solve tasks.
Agents with common memory architectures struggle to recall and integrate across multiple timesteps of a past event, or even to recall the details of a single timestep that is followed by distractor tasks.
no code implementations • • Andrea Banino, Adrià Puigdomènech Badia, Raphael Köster, Martin J. Chadwick, Vinicius Zambaldi, Demis Hassabis, Caswell Barry, Matthew Botvinick, Dharshan Kumaran, Charles Blundell
First, it introduces a separation between memories (facts) stored in external memory and the items that comprise these facts in external memory.
1 code implementation • 11 Nov 2016 • Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andrew J. Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent SIfre, Koray Kavukcuoglu, Dharshan Kumaran, Raia Hadsell
Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents.