3 citations · 4 across the 5 of their papers we have counts for
6 papers
Imagining Goals with Geometric Horizon Models for Policy Generalization
Daniel Lawson, Dhruv Sreenivas, Adriana Hugessen +2
Training from offline data has allowed for substantial progress in domains such as robotics, leading to general-purpose policies that can be easily applied zero-shot or efficiently…
Efficient Morphology-Aware Policy Transfer to New Embodiments
Michael Przystupa, Hongyao Tang, Martin Jagersand +4
Morphology-aware policy learning is a means of enhancing policy sample efficiency by aggregating data from multiple agents. These types of policies have previously been shown to he…
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…
Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of View
Raj Ghugare, Matthieu Geist, Glen Berseth +1
Some reinforcement learning (RL) algorithms can stitch pieces of experience to solve a task never seen before during training. This oft-sought property is one of the few ways in wh…
Searching for High-Value Molecules Using Reinforcement Learning and Transformers
Raj Ghugare, Santiago Miret, Adriana Hugessen +2
Reinforcement learning (RL) over text representations can be effective for finding high-value policies that can search over graphs. However, RL requires careful structuring of the…
AnyMorph: Learning Transferable Polices By Inferring Agent Morphology
Brandon Trabucco, Mariano Phielipp, Glen Berseth
The prototypical approach to reinforcement learning involves training policies tailored to a particular agent from scratch for every new morphology. Recent work aims to eliminate t…