Expected-mean gamma-incremental reinforcement learning algorithm for robot path planning

Tan, Chee Sheng; Mohd-Mokhtar, Rosmiwati; Arshad, Mohd Rizal

doi:10.1016/j.eswa.2024.123539

Expert Systems with Applications

2024

DOI: 10.1016/j.eswa.2024.123539

|View full text |Cite

Expected-mean gamma-incremental reinforcement learning algorithm for robot path planning

Chee Sheng Tan,

Rosmiwati Mohd-Mokhtar,

Mohd Rizal Arshad

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Reinforcement Learning for Autonomous Agents: Scene-Specific Dynamic Obstacle Avoidance and Target Pursuit in Unknown Environments

Tang,

Fu,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Reinforcement Learning for Autonomous Agents: Scene-Specific Dynamic Obstacle Avoidance and Target Pursuit in Unknown Environments

Tang,

Fu,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Path Planning of Autonomous Mobile Robot in Comprehensive Unknown Environment Using Deep Reinforcement Learning

Bai,

Pang,

et al. 2024

IEEE Internet Things J.

View full text Add to dashboard Cite

Architecture Design of Virtual Power Plant Intelligent Scheduling System Based on Multi-Resource Fusion

Hu,

Fan,

Xue

et al. 2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

This paper proposes a distributed scheduling model for virtual power plants, and in order to realize the optimal scheduling of multiple virtual power plants, it proposes a reinforcement learning model and establishes a framework of reinforcement learning methods to subdivide the states, actions, and rewards that are generated by the grid scheduling center when it interacts with the power grid. An environment model including multiple virtual power plant operation models is constructed, and the objective function of the control center is set to allocate scheduling commands in the virtual power plants. The algorithmic analysis is based on the actual network framework of the regional power grid and divides the tariffs of power purchased/sold by the VPP to the power market. The centralized optimization method, distributed optimization method, and reinforcement learning optimization method are proposed respectively to solve the MVPP coordinated optimal dispatch model. The optimization objective, deviation control strategy, and carbon trading elements are exemplified, and different scenario scenarios are set to analyze the convergence of the reinforcement learning model (DDPG) and the scheduling results. Reinforcement learning real-time optimal scheduling focuses on the measured values of wind power and loads and avoids the increase of costs or decrease of revenues due to the fluctuation smoothing by the grid by coordinating the internal resources for smoothing or the complementary consumption through the inter-rate of VPP. It can take into account the impact of the current decision on future time, thus achieving scheduling optimization in multiple periods.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Expected-mean gamma-incremental reinforcement learning algorithm for robot path planning

Cited by 3 publications

References 40 publications

Reinforcement Learning for Autonomous Agents: Scene-Specific Dynamic Obstacle Avoidance and Target Pursuit in Unknown Environments

Reinforcement Learning for Autonomous Agents: Scene-Specific Dynamic Obstacle Avoidance and Target Pursuit in Unknown Environments

Path Planning of Autonomous Mobile Robot in Comprehensive Unknown Environment Using Deep Reinforcement Learning

Architecture Design of Virtual Power Plant Intelligent Scheduling System Based on Multi-Resource Fusion

Contact Info

Product

Resources

About