Peter W. Battaglia — accepted AI-conference papers

2020

Combining Q-Learning and Search with Amortized Value Estimates

Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Theophane Weber, Lars Buesing +1

ICLR 2020poster

We introduce "Search with Amortized Value Estimates" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS). In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values. The new Q-est…

Cited by 65 Source ↗Scholar ↗

2019

Relational Forward Models for Multi-Agent Learning

Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinicius Zambaldi, János Kramár, Neil C. Rabinowitz +3

ICLR 2019poster

The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to…

Cited by 95 Source ↗PDF ↗Scholar ↗

2017

Metacontrol for Adaptive Imagination-Based Optimization

Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, Peter W. Battaglia

ICLR 2017poster

Many machine learning systems are built to solve the hardest examples of a particular task, which often makes them large and expensive to run---especially with respect to the easier examples, which might require much less computation. For an agent with a limited computational budget, this "one-size-…

Cited by 76 Source ↗code ↗Scholar ↗