122 citations · 436 across the 20 of their papers we have counts for
28 papers
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Riashat Islam, Hongyu Zang, Anirudh Goyal +6
Goal-conditioned reinforcement learning (RL) is a promising direction for training agents that are capable of solving multiple tasks and reach a diverse set of objectives. How to \…
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel
Dianbo Liu, Vedant Shah, Oussama Boussif +6
In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one another. In this paper, we propose an alt…
Retrieval-Augmented Reinforcement Learning
Anirudh Goyal, Abram L. Friesen, Andrea Banino +13
Most deep reinforcement learning (RL) algorithms distill experience into parametric behavior policies or value functions via gradient updates. While effective, this approach has se…
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste +4
Learning the causal structure that underlies data is a crucial step towards robust real-world decision making. The majority of existing work in causal inference focuses on determin…
Fast and Slow Learning of Recurrent Independent Mechanisms
Kanika Madan, Nan Rosemary Ke, Anirudh Goyal +2
Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning agent interacting with its environment is l…
Transformers with Competitive Ensembles of Independent Mechanisms
Alex Lamb, Di He, Anirudh Goyal +4
An important development in deep learning from the earliest MLPs has been a move towards architectures with structural inductive biases which enable the model to keep distinct sour…