3 papers
cs.RO2022
Sample Efficient Robot Learning with Structured World Models
Tuluhan Akbulut, Max Merlin, Shane Parr +2
Reinforcement learning has been demonstrated as a flexible and effective approach for learning a range of continuous control tasks, such as those used by robots to manipulate objec…
cs.RO2021
Agent-aware State Estimation in Autonomous Vehicles
Shane Parr, Ishan Khatri, Justin Svegliato +1
Autonomous systems often operate in environments where the behavior of multiple agents is coordinated by a shared global state. Reliable estimation of the global state is thus crit…
cs.LG2019
Planning with Abstract Learned Models While Learning Transferable Subtasks
John Winder, Stephanie Milani, Matthew Landen +5
We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple…