4 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.AI2021
Is Policy Learning Overrated?: Width-Based Planning and Active Learning for Atari
Benjamin Ayton, Masataro Asai
Width-based planning has shown promising results on Atari 2600 games using pixel input, while using substantially fewer environment interactions than reinforcement learning. Recent…
eess.SY2018★ 1 cited
RADMPC: A Fast Decentralized Approach for Chance-Constrained Multi-Vehicle Path-Planning
Aaron Huang, Benjamin J. Ayton, Brian C. Williams
Robust multi-vehicle path-planning is important for ensuring the safety of multi-vehicle systems in applications like transportation, search and rescue, and robotic exploration. Ch…
cs.AI2018★ 4 cited
Vulcan: A Monte Carlo Algorithm for Large Chance Constrained MDPs with Risk Bounding Functions
Benjamin J Ayton, Brian C Williams
Chance Constrained Markov Decision Processes maximize reward subject to a bounded probability of failure, and have been frequently applied for planning with potentially dangerous o…