Friday, 15:15 - 15:40 h, Room: H 0107

 

Quentin Louveaux
Relaxation schemes for the evaluation of a policy in batch mode reinforcement learning

Coauthors: Bernard Boigelot, Damien Ernst, Raphaƫl Fonteneau

 

Abstract:
We study the min max optimization problem introduced for computing policies for batch mode reinforcement learning in a deterministic setting. First, we show that this problem is NP-hard. In the two-stage case, we provide two relaxation schemes. The first relaxation scheme works by dropping some constraints in order to obtain a problem that is solvable in polynomial time. The second relaxation scheme, based on a Lagrangian relaxation where all constraints are dualized, leads to a conic quadratic programming problem. We also theoretically prove and empirically illustrate that both relaxation schemes provide better results than those given previously for the same problem.

 

Talk 1 of the contributed session Fri.3.H 0107
"Decomposition and relaxation methods" [...]
Cluster 16
"Nonlinear programming" [...]

 

  Payday Loans In North Carolina. In this section we give only a brief summary recommendation for admission of Cheap Levitra. Full information can be found in the instructions for receiving medications with vardenafil.