Abstract: With the rapidly increase demand on reliable wireless communication for rail scenarios, especially for real-time services, the resource limitations of on-board users are becoming ...
The algorithm consists of two networks, an Actor and a Critic network, which approximate the policy and value functions of a reinforcement learning problem. The name DDPG, or Deep Deterministic Policy ...
Abstract: This paper focused on three application problems of the traditional Deep Deterministic Policy Gradient(DDPG) algorithm. That is, the agent exploration is insufficient, the neural network ...
This paper investigates the potential of the intrinsically motivated reinforcement learning (IMRL) approach for robotic drumming. For this purpose, we implemented an IMRL-based algorithm for a ...
The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...
With the advancement of technology and the continuous innovation of applications, low-latency applications such as drones, online games and virtual reality are gradually becoming popular demands in ...
The performance of the state-of-the-art Deep Reinforcement algorithms such as Proximal Policy Optimization, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic for generating a ...
To address the limitations of the traditional maglev control algorithm that relies on mathematical models and is difficult to deploy, a single-point maglev system controller with improved DDPG based ...
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