55 citations · 111 across the 10 of their papers we have counts for
10 papers
Structured State Space Models for In-Context Reinforcement Learning
Chris Lu, Yannick Schroecker, Albert Gu +4
Structured state space sequence (S4) models have recently achieved state-of-the-art performance on long-range sequence modeling tasks. These models also have fast inference speeds…
Human-Timescale Adaptation in an Open-Ended Task Space
Adaptive Agent Team, Jakob Bauer, Kate Baumli +25
Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to rein…
Meta-Gradients in Non-Stationary Environments
Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker +3
Meta-gradient methods (Xu et al., 2018; Zahavy et al., 2020) offer a promising solution to the problem of hyperparameter selection and adaptation in non-stationary reinforcement le…
Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality
Tom Zahavy, Yannick Schroecker, Feryal Behbahani +4
Finding different solutions to the same problem is a key aspect of intelligence associated with creativity and adaptation to novel situations. In reinforcement learning, a set of d…
Bootstrapped Meta-Learning
Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy +3
Meta-learning empowers artificial intelligence to increase its efficiency by learning how to learn. Unlocking this potential involves overcoming a challenging meta-optimisation pro…
Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement Learning
Yannick Schroecker, Charles Isbell
This work considers two distinct settings: imitation learning and goal-conditioned reinforcement learning. In either case, effective solutions require the agent to reliably reach a…
Active Learning within Constrained Environments through Imitation of an Expert Questioner
Kalesha Bullard, Yannick Schroecker, Sonia Chernova
Active learning agents typically employ a query selection algorithm which solely considers the agent's learning objectives. However, this may be insufficient in more realistic huma…
Generative predecessor models for sample-efficient imitation learning
Yannick Schroecker, Mel Vecerik, Jonathan Scholz
We propose Generative Predecessor Models for Imitation Learning (GPRIL), a novel imitation learning algorithm that matches the state-action distribution to the distribution observe…
Imitating Latent Policies from Observation
Ashley D. Edwards, Himanshu Sahni, Yannick Schroecker +1
In this paper, we describe a novel approach to imitation learning that infers latent policies directly from state observations. We introduce a method that characterizes the causal…
State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning
Himanshu Sahni, Saurabh Kumar, Farhan Tejani +2
Typical reinforcement learning (RL) agents learn to complete tasks specified by reward functions tailored to their domain. As such, the policies they learn do not generalize even t…