30 citations · 94 across the 10 of their papers we have counts for
4 papers · 1 filter
Evaluating task-agnostic exploration for fixed-batch learning of arbitrary future tasks
Vibhavari Dasagi, Robert Lee, Jake Bruce +1
Deep reinforcement learning has been shown to solve challenging tasks where large amounts of training experience is available, usually obtained online while learning the task. Robo…
Ctrl-Z: Recovering from Instability in Reinforcement Learning
Vibhavari Dasagi, Jake Bruce, Thierry Peynot +1
When learning behavior, training data is often generated by the learner itself; this can result in unstable training dynamics, and this problem has particularly important applicati…
Coordinated Heterogeneous Distributed Perception based on Latent Space Representation
Timo Korthals, Jürgen Leitner, Ulrich Rückert
We investigate a reinforcement approach for distributed sensing based on the latent space derived from multi-modal deep generative models. Our contribution provides insights to the…
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs
Vibhavari Dasagi, Robert Lee, Serena Mou +3
Current end-to-end deep Reinforcement Learning (RL) approaches require jointly learning perception, decision-making and low-level control from very sparse reward signals and high-d…