277 citations · 953 across the 11 of their papers we have counts for
13 papers
De novo design of high-affinity protein binders with AlphaProteo
Vinicius Zambaldi, David La, Alexander E. Chu +29
Computational design of protein-binding proteins is a fundamental capability with broad utility in biomedical research and biotechnology. Recent methods have made strides against s…
The Advantage Regret-Matching Actor-Critic
Audrūnas Gruslys, Marc Lanctot, Rémi Munos +10
Regret minimization has played a key role in online learning, equilibrium computation in games, and reinforcement learning (RL). In this paper, we describe a general model-free RL…
MEMO: A Deep Network for Flexible Combination of Episodic Memories
Andrea Banino, Adrià Puigdomènech Badia, Raphael Köster +7
Recent research developing neural network architectures with external memory have often used the benchmark bAbI question and answering dataset which provides a challenging number o…
OpenSpiel: A Framework for Reinforcement Learning in Games
Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau +24
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi…
CompILE: Compositional Imitation Learning and Execution
Thomas Kipf, Yujia Li, Hanjun Dai +5
We introduce Compositional Imitation Learning and Execution (CompILE): a framework for learning reusable, variable-length segments of hierarchically-structured behavior from demons…
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi +4
Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence. Among the most common approaches are algo…