5 papers
Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
Angeliki Kamoutsi, Peter Schmitt-Förster, Tobias Sutter +2
This work studies discrete-time discounted Markov decision processes with continuous state and action spaces and addresses the inverse problem of inferring a cost function from obs…
Proximal Point Imitation Learning
Luca Viano, Angeliki Kamoutsi, Gergely Neu +2
This work develops new algorithms with rigorous efficiency guarantees for infinite horizon imitation learning (IL) with linear function approximation without restrictive coherence…
Stochastic convex optimization for provably efficient apprenticeship learning
Angeliki Kamoutsi, Goran Banjac, John Lygeros
We consider large-scale Markov decision processes (MDPs) with an unknown cost function and employ stochastic convex optimization tools to address the problem of imitation learning,…
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
Angeliki Kamoutsi, Goran Banjac, John Lygeros
We consider large-scale Markov decision processes with an unknown cost function and address the problem of learning a policy from a finite set of expert demonstrations. We assume t…
On Infinite Linear Programming and the Moment Approach to Deterministic Infinite Horizon Discounted Optimal Control Problems
Angeliki Kamoutsi, Tobias Sutter, Peyman Mohajerin Esfahani +1
We revisit the linear programming approach to deterministic, continuous time, infinite horizon discounted optimal control problems. In the first part, we relax the original problem…