Proximal policy optimization with adaptive threshold for symmetric relative density ratio
Deep reinforcement learning (DRL) is one of the promising approaches for introducing robots into complicated environments. The recent remarkable progress of DRL stands on regularization of policy, which allows the policy to improve stably and efficiently. A popular method, so-called proximal policy...
Main Author: | Taisuke Kobayashi |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2023-03-01
|
Series: | Results in Control and Optimization |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720722000649 |
Similar Items
-
Proximal Policy Optimization Based on Self-directed Action Selection
by: SHEN Yi, LIU Quan
Published: (2021-12-01) -
An Empirical Investigation of Early Stopping Optimizations in Proximal Policy Optimization
by: Rousslan Fernand Julien Dossa, et al.
Published: (2021-01-01) -
Deep Adversarial Reinforcement Learning Method to Generate Control Policies Robust Against Worst-Case Value Predictions
by: Kohei Ohashi, et al.
Published: (2023-01-01) -
An Object Recognition Grasping Approach Using Proximal Policy Optimization With YOLOv5
by: Qingchun Zheng, et al.
Published: (2023-01-01) -
Comparison of On-Policy Deep Reinforcement Learning A2C with Off-Policy DQN in Irrigation Optimization: A Case Study at a Site in Portugal
by: Khadijeh Alibabaei, et al.
Published: (2022-06-01)