Residual reinforcement learning for robot control
Conventional feedback control methods can solve various types of robot control problems
very efficiently by capturing the structure with explicit models, such as rigid body equations …
very efficiently by capturing the structure with explicit models, such as rigid body equations …
Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus
Hybrid electric vehicles offer an immediate solution for emissions reduction and fuel
displacement under the current technique level. Energy management strategies are critical …
displacement under the current technique level. Energy management strategies are critical …
Generalization in reinforcement learning with selective noise injection and information bottleneck
The ability for policies to generalize to new environments is key to the broad application of
RL agents. A promising approach to prevent an agent's policy from overfitting to a limited set …
RL agents. A promising approach to prevent an agent's policy from overfitting to a limited set …
Recurrent model-free rl can be a strong baseline for many pomdps
Many problems in RL, such as meta-RL, robust RL, generalization in RL, and temporal credit
assignment, can be cast as POMDPs. In theory, simply augmenting model-free RL with …
assignment, can be cast as POMDPs. In theory, simply augmenting model-free RL with …
A bibliometric analysis and review on reinforcement learning for transportation applications
Transportation is the backbone of the economy and urban development. Improving the
efficiency, sustainability, resilience, and intelligence of transportation systems is critical and …
efficiency, sustainability, resilience, and intelligence of transportation systems is critical and …
Deep reinforcement learning based direct torque control strategy for distributed drive electric vehicles considering active safety and energy saving performance
Distributed drive electric vehicles are regarded as a broadly promising transportation tool
owing to their convenience and maneuverability. However, reasonable and efficient …
owing to their convenience and maneuverability. However, reasonable and efficient …
多Agent 深度强化学习综述
梁星星， 冯旸赫， 马扬， 程光权， 黄金才， 王琦， 周玉珍… - 自动化学报, 2020 - aas.net.cn
多Agent深度强化学习综述 E-mail Alert RSS 2.765 2022影响因子 (CJCR) 中文核心 EI 中国科技
核心 Scopus CSCD 英国科学文摘 首页 期刊介绍 1.基本信息 2.收录与获奖 3.近年指标 期刊在线 …
核心 Scopus CSCD 英国科学文摘 首页 期刊介绍 1.基本信息 2.收录与获奖 3.近年指标 期刊在线 …
Regularization matters in policy optimization
Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention
thanks to its encouraging performance on a variety of control tasks. Yet, conventional …
thanks to its encouraging performance on a variety of control tasks. Yet, conventional …
Taurus: a data plane architecture for per-packet ML
Emerging applications---cloud computing, the internet of things, and augmented/virtual
reality---demand responsive, secure, and scalable datacenter networks. These networks …
reality---demand responsive, secure, and scalable datacenter networks. These networks …
[PDF][PDF] Structure in reinforcement learning: A survey and open problems
Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …
Networks (DNNs) for function approximation, has demonstrated considerable success in …