21 citations · 36 across the 4 of their papers we have counts for
7 papers
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning
Giambattista Parascandolo, Lars Buesing, Josh Merel +6
Standard planners for sequential decision making (including Monte Carlo planning, tree search, dynamic programming, etc.) are constrained by an implicit sequential planning assumpt…
Behaviour Suite for Reinforcement Learning
Ian Osband, Yotam Doron, Matteo Hessel +11
This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabiliti…
When to use parametric models in reinforcement learning?
Hado van Hasselt, Matteo Hessel, John Aslanides
We examine the question of when and how parametric models are most useful in reinforcement learning. In particular, we look at commonalities and differences between parametric mode…
TF-Replicator: Distributed Machine Learning for Researchers
Peter Buchlovsky, David Budden, Dominik Grewe +9
We describe TF-Replicator, a framework for distributed machine learning designed for DeepMind researchers and implemented as an abstraction over TensorFlow. TF-Replicator simplifie…
Universal Reinforcement Learning Algorithms: Survey and Experiments
John Aslanides, Jan Leike, Marcus Hutter
Many state-of-the-art reinforcement learning (RL) algorithms typically assume that the environment is an ergodic Markov Decision Process (MDP). In contrast, the field of universal…
AIXIjs: A Software Demo for General Reinforcement Learning
John Aslanides
Reinforcement learning is a general and powerful framework with which to study and implement artificial intelligence. Recent advances in deep learning have enabled RL algorithms to…