Results 21 to 30 of about 6,304,901 (198)

Proximal policy optimization (PPO) algorithm's role in autonomous decision-making for autism

Results in Engineering
Surendran Rajendran
exaly   +2 more sources

Off-Policy Proximal Policy Optimization

, 2023
Proximal Policy Optimization (PPO) is an important reinforcement learning method, which has achieved great success in sequential decision-making problems. However, PPO faces the issue of sample inefficiency, which is due to the PPO cannot make use of off-
Yin, Yilong, Meng, Wenjia, Zheng, Qian, Pan, Gang   +3 more
core   +1 more source

A Novel Intelligent Anti-Jamming Algorithm Based on Deep Reinforcement Learning Assisted by Meta-Learning for Wireless Communication Systems

Applied Sciences, 2023
In the field of intelligent anti-jamming, deep reinforcement learning algorithms are regarded as key technical means. However, the learning process of deep reinforcement learning algorithms requires a stable learning environment to ensure its ...
Qingchuan Chen, Yingtao Niu, Boyu Wan, Peng Xiang   +3 more
doaj   +1 more source

Proximal Policy Optimization with Relative Pearson Divergence [PDF]

, 2021
The recent remarkable progress of deep reinforcement learning (DRL) stands on regularization of policy for stable and efficient learning. A popular method, named proximal policy optimization (PPO), has been introduced for this purpose.
Kobayashi, Taisuke
core   +1 more source

Multiple-UAV Reinforcement Learning Algorithm Based on Improved PPO in Ray Framework

Drones, 2022
Distributed multi-agent collaborative decision-making technology is the key to general artificial intelligence. This paper takes the self-developed Unity3D collaborative combat environment as the test scenario, setting a task that requires heterogeneous ...
Guang Zhan, Xinmiao Zhang, Zhongchao Li, Lin Xu, Deyun Zhou, Zhen Yang   +5 more
doaj   +1 more source

Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy [PDF]

, 2023
Proximal policy optimization and trust region policy optimization (PPO and TRPO) with actor and critic parametrized by neural networks achieve significant empirical success in deep reinforcement learning.
Liu, Boyi, Cai, Qi, Yang, Zhuoran, Wang, Zhaoran   +3 more
core   +1 more source

Pairwise Proximal Policy Optimization: Harnessing Relative Feedback for LLM Alignment [PDF]

, 2023
Large Language Models (LLMs) can acquire extensive world knowledge through pre-training on large corpora. However, due to exposure to low-quality data, LLMs may exhibit harmful behavior without aligning with human values.
Zhang, Ruoyu, Wu, Tianhao, Zhu, Banghua, Ramchandran, Kannan, Wen, Zhaojin, Jiao, Jiantao   +5 more
core   +1 more source

Minimax and entropic proximal policy optimization [PDF]

, 2023
First-order gradient descent is to date the most commonly used optimization method for training deep neural networks, especially for networks with shared parameters, or recurrent neural networks (RNNs).
Song, Yunlong
core   +1 more source

Eval-PPO: Building an Efficient Threat Evaluator Using Proximal Policy Optimization

CoRR
The research content is not yet complete and requires further supplementation and ...
Sun, Wuzhou, Li, Siyi, Zou, Qingxiang, Liao, Zixing   +3 more
openaire   +5 more sources

An Interval Incentive and Predictive Interpolation-Based PPO Method for AGV Path Planning

Actuators
This work proposes an interval incentive and predictive interpolation-based proximal policy optimization (IP-PPO) scheme for automated guided vehicle (AGV) path planning, to achieve fast convergence, strong generalization and sufficient smoothness in the
Pengyang Liao, Ling Meng, Pengyu Guo, Bo Li   +3 more
doaj   +1 more source