… on an appealing paradigm of training Reinforcement Learning Systems for Multiple Agents
known as Decentralized Actor, Centralized Critic. The core idea behind this paradigm is sum…

… ) Paradigm: We develop a reinforcement learning paradigm of configuring visual enhancement
for object detection in underwater scenes. The novel paradigm … , the paradigm selects the …

… We tested the paradigm, which we call robust reinforcement learning (RRL), on the control
task … a new reinforcement learning paradigm that we call robust reinforcement learning (RRL). …

… of deep reinforcement learning in wireless networks, such as the deployment of cognitive
radio, we introduced DRL as a paradigm … , Q-learning, as an intelligent paradigm for MAC layer …

… In this chapter, the potentials of deep reinforcement learning paradigm are studied for
performance optimization of channel observation-based MAC protocols (ie, COSB) in dense …

… Figure 2 illustrates the basic components of our genetic reinforcement learning paradigm.
A real-valued string determines the weights in a neural network of predefined size and con…

… This paper proposes a new reinforcement learning (RL) paradigm that explicitly takes into
… a new reinforcement learning paradigm that we call \Robust Reinforcement Learning (RRL).…

… In this paper, we examine how Non-Markov Reinforcement Learning (NMRL) techniques
can apply to modeling natural agents, and we investigate an alternative model called the …

… Reinforcement learning is a learning paradigm concerned with learning to control a system
so as … What distinguishes reinforcement learning from supervised learning is that only partial …

… Reinforcement learning (RL) is a framework of particular importance to psychology, neuroscience
and machine learning… of RL, has facilitated paradigm shifts that relate multiple levels of …