We explore reinforcement learning methods for finding the optimal policy in the linear quadratic regulator (LQR) problem. In particular we consider the convergence of policy gradient methods in the setting of known and unknown parameters. We are able to produce a global linear convergence...

An uninformed sender designs a mechanism that discloses information about her type to a privately informed receiver, who then decides whether to act. I impose a single-crossing assumption, so that the receiver with a higher type is more willing to act. Using a linear programming approach, I...

In this paper we suggest a new efficient technique for solving integer knapsack problems. Our algorithms can be seen as application of Fast Fourier Transform to generating functions of integer polytopes. Using this approach, it is possible to count the number of boolean solutions of a single...

An uninformed sender designs a mechanism that discloses information about her type to a privately informed receiver, who then decides whether to act. I impose a single-crossing assumption, so that the receiver with a higher type is more willing to act. Using a linear programming approach, I...

We consider a “tenure-clock problem” in which a principal may set a deadline by which she needs to evaluate an agent's ability and decides whether to promote him or not. We embed this problem in a continuous-time model with both hidden action and hidden information, where the principal must...

I examine how the communication incentive of an agent (sender) changes when the prior of the principal (receiver) about the agent's private information becomes more optimistic (in the sense of monotone likelihood ratio dominance). I use the canonical model of strategic communication (Crawford...