Safe reinforcement learning for real-time automatic control in a smart energy-hub

2022 ◽  
Vol 309 ◽  
pp. 118403
Author(s):  
Dawei Qiu ◽  
Zihang Dong ◽  
Xi Zhang ◽  
Yi Wang ◽  
Goran Strbac
Keyword(s):  
Reinforcement Learning ◽  
Automatic Control ◽  
Real Time ◽  
Energy Hub

Reinforcement Learning Strategy for Automatic Control of Real-time Obstacle Avoidance based on Vehicle Dynamics

2017 ◽  
Vol 12 (3) ◽  
pp. 297-305 ◽  
Author(s):  
Dong-Hoon Kang ◽  
◽  
Jae Hwan Bong ◽  
Jooyoung Park ◽  
Shinsuk Park
Keyword(s):  
Reinforcement Learning ◽  
Automatic Control ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Vehicle Dynamics ◽  
Learning Strategy

scholarly journals Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3864
Author(s):  
Tarek Ghoul ◽  
Tarek Sayed
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Low Cost ◽  
Safety Evaluation ◽  
Traffic Volume ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Real World Data ◽  
Physical Constraints ◽  
Traffic Conflicts

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic speed advisories in real-time that guide vehicles towards an optimum speed. Real-time safety evaluation models for signalized intersections that depend on dynamic traffic parameters such as traffic volume and shock wave characteristics were used for this purpose. The proposed algorithm incorporates a rule-based approach alongside a Deep Deterministic Policy Gradient reinforcement learning technique (DDPG) to assign ideal speeds for connected vehicles at intersections and improve safety. The system was tested on two intersections using real-world data and yielded an average reduction in traffic conflicts ranging from 9% to 23%. Further analysis was performed to show that the algorithm yields tangible results even at lower market penetration rates (MPR). The algorithm was tested on the same intersection with different traffic volume conditions as well as on another intersection with different physical constraints and characteristics. The proposed algorithm provides a low-cost approach that is not computationally intensive and works towards optimizing for safety by reducing rear-end traffic conflicts.

scholarly journals Real Time Path Planning of Robot using Deep Reinforcement Learning

2020 ◽  
Vol 53 (2) ◽  
pp. 15602-15607
Author(s):  
Jeevan Raajan ◽  
P V Srihari ◽  
Jayadev P Satya ◽  
B Bhikkaji ◽  
Ramkrishna Pasumarthy
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Real Time ◽  
Time Path

scholarly journals Deep Reinforcement Learning for End-to-End Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2534
Author(s):  
Oualid Doukhi ◽  
Deok-Jin Lee
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Autonomous Navigation ◽  
Control Technique ◽  
Local Motion ◽  
Aerial Robots ◽  
Novel Approach ◽  
Outdoor Environments ◽  
Aerial Vehicle ◽  
High Level

Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.

scholarly journals A Real-Time Rolling Horizon Chance Constrained Optimization Model for Energy Hub Scheduling

2020 ◽  
Vol 62 ◽  
pp. 102417
Author(s):  
Weilin Hou ◽  
Zhaoxi Liu ◽  
Li Ma ◽  
Lingfeng Wang
Keyword(s):  
Constrained Optimization ◽  
Real Time ◽  
Optimization Model ◽  
Rolling Horizon ◽  
Energy Hub

Self-regulation of the anterior insula: Reinforcement learning using real-time fMRI neurofeedback

NeuroImage ◽  
2014 ◽  
Vol 88 ◽  
pp. 113-124 ◽  
Author(s):  
Emma J. Lawrence ◽  
Li Su ◽  
Gareth J. Barker ◽  
Nick Medford ◽  
Jeffrey Dalton ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Self Regulation ◽  
Anterior Insula
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