The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.
( Image credit: Playing Atari with Deep Reinforcement Learning )
In the fourth section, the author surveyed sets of game-theoretic frameworks that researchers used to address this problem for resource allocation and task scheduling in the wireless sensor networks.
Second, the overall design space composed of HW/SW partitioning, hardware optimization, and software optimization is huge.
The result shows that the proposed protocol has better performance in terms of message delivery ratio and overhead.
In this paper we present a new approach to tackle complex routing problems with an improved state representation that utilizes the model complexity better than previous methods.
Recently, Intelligent Transportation Systems are leveraging the power of increased sensory coverage and computing power to deliver data-intensive solutions achieving higher levels of performance than traditional systems.
Deep Reinforcement Learning (DRL) is gaining attention as a potential approach to design trajectories for autonomous unmanned aerial vehicles (UAV) used as flying access points in the context of cellular or Internet of Things (IoT) connectivity.
People with Alzheimer's disease and related dementias (ADRD) often show the problem of repetitive questioning, which brings a great burden on persons with ADRD (PwDs) and their caregivers.
We consider the problem of learning useful robotic skills from previously collected offline data without access to manually specified rewards or additional online exploration, a setting that is becoming increasingly important for scaling robot learning by reusing past robotic data.
We consider a prospect theoretic version of the classical Q-learning algorithm for discounted reward Markov decision processes, wherein the controller perceives a distorted and noisy future reward, modeled by a nonlinearity that accentuates gains and underrepresents losses relative to a reference point.
We address these problems by proving a new group-theoretic result in the context of equivariant neural networks that shows that a network is equivariant to a large group if and only if it is equivariant to smaller groups from which it is constructed.