32 citations · 108 across the 28 of their papers we have counts for
7 papers · 1 filter
Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning
Daniel Lawson, Adriana Hugessen, Charlotte Cloutier +2
While goal-conditioned behavior cloning (GCBC) methods can perform well on in-distribution training tasks, they do not necessarily generalize zero-shot to tasks that require condit…
Plasticity as the Mirror of Empowerment
David Abel, Michael Bowling, André Barreto +13
Agents are minimally entities that are influenced by their past observations and act to influence future observations. This latter capacity is captured by empowerment, which has se…
Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning
Lynn Cherif, Flemming Kondrup, David Venuto +3
Agents that can autonomously navigate the web through a graphical user interface (GUI) using a unified action space (e.g., mouse and keyboard actions) can require very large amount…
Representation Learning via Non-Contrastive Mutual Information
Zhaohan Daniel Guo, Bernardo Avila Pires, Khimya Khetarpal +2
Labeling data is often very time consuming and expensive, leaving us with a majority of unlabeled data. Self-supervised representation learning methods such as SimCLR (Chen et al.,…
Long Range Navigator (LRN): Extending robot planning horizons beyond metric maps
Matt Schmittle, Rohan Baijal, Nathan Hatch +6
A robot navigating an outdoor environment with no prior knowledge of the space must rely on its local sensing to perceive its surroundings and plan. This can come in the form of a…
Agency Is Frame-Dependent
David Abel, André Barreto, Michael Bowling +13
Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science, and artificial intelligence. Determini…
Optimizing Return Distributions with Distributional Dynamic Programming
Bernardo Ávila Pires, Mark Rowland, Diana Borsa +6
We introduce distributional dynamic programming (DP) methods for optimizing statistical functionals of the return distribution, with standard reinforcement learning as a special ca…