A Confrontation Decision-Making Method with Deep Reinforcement Learning and Knowledge Transfer for Multi-Agent System
In this paper, deep reinforcement learning (DRL) and knowledge transfer are used to achieve the effective control of the learning agent for the confrontation in the multi-agent systems. Firstly, a multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm with parameter sharing is proposed to a...
Main Author: | Chunyang Hu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-04-01
|
Series: | Symmetry |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-8994/12/4/631 |
Similar Items
-
Reinforcement Learning Your Way: Agent Characterization through Policy Regularization
by: Charl Maree, et al.
Published: (2022-03-01) -
Process control of mAb production using multi-actor proximal policy optimization
by: Nikita Gupta, et al.
Published: (2023-09-01) -
Hierarchical DDPG for Manipulator Motion Planning in Dynamic Environments
by: Dugan Um, et al.
Published: (2022-08-01) -
Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin
by: Eneko Artetxe, et al.
Published: (2023-05-01) -
Exploration with Multiple Random ε-Buffers in Off-Policy Deep Reinforcement Learning
by: Chayoung Kim, et al.
Published: (2019-11-01)