Q-learning, a most widely used reinforcement learning method, normally needs well-defined quantized state and action spaces to converge. This makes it difficult to be applied to re...
Exploitation ofinstruction-levelparallelism is an ejfective mechanism for improving the performance of modern super-scalar/VLIW processors. Various software techniques can be appl...
In this paper, we seek to enhance the poor performance of original TCP in wireless multi-hop environments due to the intra-flow contention between TCP-DATA and TCP-ACK packets. As...
Recently, a new iterative optimization framework utilizing an evolutionary algorithm called "Prototype Optimization with Evolved iMprovement Steps" (POEMS) was introduced...
The aim of this paper is to enhance the performance of a reinforcement learning game agent controller, within a dynamic game environment, through the retention of learned informati...