Distributed Policy Evaluation with Fractional Order Dynamics in Multiagent Reinforcement Learning
The main objective of multiagent reinforcement learning is to achieve a global optimal policy. It is difficult to evaluate the value function with high-dimensional state space. Therefore, we transfer the problem of multiagent reinforcement learning into a distributed optimization problem with constr...
Main Authors: | Wei Dai, Wei Wang, Zhongtian Mao, Ruwen Jiang, Fudong Nian, Teng Li |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi-Wiley
2021-01-01
|
Series: | Security and Communication Networks |
Online Access: | http://dx.doi.org/10.1155/2021/1020466 |
Similar Items
-
Policy Distillation and Value Matching in Multiagent Reinforcement Learning
by: Wadhwania, Samir, et al.
Published: (2021) -
Consensus Tracking of Fractional-Order Multiagent Systems via Fractional-Order Iterative Learning Control
by: Shuaishuai Lv, et al.
Published: (2019-01-01) -
Multiagent Reinforcement Learning Dynamic Spectrum Access in Cognitive Radios
by: Wu Chun, et al.
Published: (2014-02-01) -
Consensus Analysis of Fractional-Order Multiagent Systems with Double-Integrator
by: Chunde Yang, et al.
Published: (2017-01-01) -
Consensus of Fractional-Order Multiagent Systems with Nonuniform Time Delays
by: Jun Liu, et al.
Published: (2018-01-01)