Averaged Soft Actor-Critic for Deep Reinforcement Learning
With the advent of the era of artificial intelligence, deep reinforcement learning (DRL) has achieved unprecedented success in high-dimensional and large-scale artificial intelligence tasks. However, the insecurity and instability of the DRL algorithm have an important impact on its performance. The...
Main Authors: | Feng Ding, Guanfeng Ma, Zhikui Chen, Jing Gao, Peng Li |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi-Wiley
2021-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2021/6658724 |
Similar Items
-
Real-Time Bidding with Soft Actor-Critic Reinforcement Learning in Display Advertising
by: Daria Yakovleva, et al.
Published: (2019-11-01) -
Path Planning for Multi-Arm Manipulators Using Deep Reinforcement Learning: Soft Actor–Critic with Hindsight Experience Replay
by: Evan Prianto, et al.
Published: (2020-10-01) -
The Actor-Dueling-Critic Method for Reinforcement Learning
by: Menghao Wu, et al.
Published: (2019-03-01) -
Development of a Soft Actor Critic deep reinforcement learning approach for harnessing energy flexibility in a Large Office building
by: Anjukan Kathirgamanathan, et al.
Published: (2021-09-01) -
Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems
by: Chien-Liang Liu, et al.
Published: (2020-01-01)