2 citations · 2 across the 9 of their papers we have counts for
3 papers · 1 filter
Towards safe and tractable Gaussian process-based MPC: Efficient sampling within a sequential quadratic programming framework
Manish Prajapat, Amon Lahr, Johannes Köhler +2
Learning uncertain dynamics models using Gaussian process~(GP) regression has been demonstrated to enable high-performance and safety-aware control strategies for challenging real-…
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
Riccardo De Santi, Federico Arangath Joseph, Noah Liniger +2
How can a scientist use a Reinforcement Learning (RL) algorithm to design experiments over a dynamical system's state space? In the case of finite and Markovian systems, an area ca…
Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods
Riccardo De Santi, Manish Prajapat, Andreas Krause
In classic Reinforcement Learning (RL), the agent maximizes an additive objective of the visited states, e.g., a value function. Unfortunately, objectives of this type cannot model…