Abstract. Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for...
- The future of the Semantic Web envisions an interconnected network of data and systems where software agents can communicate seamlessly to perform complicated tasks with limited ...
Danny Chen, John Lastusky, James Starz, Stephen Ho...
A new and efficient class of nonlinear receivers is introduced for digital communication systems. These "iterated-decision" receivers use optimized multipass algorithms t...
Dramatic progress has been made in algorithms for placement and routing over the last 5 years, with improvements in both speed and quality. Combining placement and routing into a ...
Jarrod A. Roy, Natarajan Viswanathan, Gi-Joon Nam,...
eously demand shorter and less costly design cycles. Designing at higher levels of abstraction makes both objectives achievable, but enabling techniques like behavioral synthesis h...