The 2007 IEEE CEC simulated car racing competition

This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process, we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competitio

A High-Speed Reconfigurable Architecture for Heterogeneous Multimodal Packet Traffic Generation

Traffic modeling plays a key role in the study of packet switching systems, such as Internet routers. As line rates increase towards tens of gigabits per second, the duration of individual packets decreases, rendering real-time traffic generation a fundamental engineering challenge. In evaluation of these systems, it is critical to reproduce traffic conditions that approximate the target environment. Additionally, the ability to generate traffic flows that establish the limitations of a given algorithm or architecture is highly desirable. To address these issues, we propose a reconfigurable high-speed hardware architecture for heterogeneous multimodal packet generation. FPGA results demonstrate the scalability and flexibility of the proposed framework

A scalable model-free recurrent neural network framework for solving POMDPs

This paper presents a framework for obtaining an optimal policy in model-free Partially Observable Markov Decision Problems (POMDPs) using a recurrent neural network (RNN). A Q-function approximation approach is taken, utilizing a novel RNN architecture with computation and storage requirements that are dramatically reduced when compared to existing schemes. A scalable online training algorithm, derived from the real-time recurrent learning (RTRL) algorithm, is employed. Moreover, stochastic meta-descent (SMD), an adaptive step size scheme for stochastic gradient-descent problems, is utilized as means of incorporating curvature information to accelerate the learning process. We consider case studies of POMDPs where state information is not directly available to the agent. Particularly, we investigate scenarios in which the agent receives indentical observations for multiple states, thereby relying on temporal dependencies captured by the RNN to obtain the optimal policy. Simulation results illustrate the effectiveness of the approach along with substantial improvement in convergence rate when compared to existing schemes