Results 1 to 10 of about 2,179 (150)

Implementing action mask in proximal policy optimization (PPO) algorithm

ICT Express, 2020
The proximal policy optimization (PPO) algorithm is a promising algorithm in reinforcement learning. In this paper, we propose to add an action mask in the PPO algorithm. The mask indicates whether an action is valid or invalid for each state. Simulation
Cheng-Yen Tang, Chien-Hung Liu, Woei-Kae Chen, Shingchern D. You   +3 more
doaj   +5 more sources

PPO-GPR: A Custom Proximal Policy Optimization Tool for Active Reinforcement Learning [PDF]

ACS Engineering Au
Abstract Efficient data selection is critical in domains where data acquisition is expensive and time-consuming, such as material science. In this work, we introduce a novel active learning framework that integrates proximal policy optimization (PPO) with Gaussian process regression (GPR) to strategically select informative data ...
Etinosa Osaro, Yamil J. Colón
doaj   +3 more sources

PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation [PDF]

2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), 2020
This paper has been accepted to IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2020).
Perttu Hämäläinen, Amin Babadi
exaly   +7 more sources

Process control of mAb production using multi-actor proximal policy optimization

Digital Chemical Engineering, 2023
Monoclonal antibodies (mAb) are biopharmaceutical products that improve human immunity. In this work, we propose a multi-actor proximal policy optimization-based reinforcement learning (RL) for the control of mAb production. Here, manipulated variable is
Nikita Gupta, Shikhar Anand, Tanuja Joshi, Deepak Kumar, Manojkumar Ramteke, Hariprasad Kodamana   +5 more
doaj   +3 more sources

An Improved Proximal Policy Optimization Method for Low-Level Control of a Quadrotor

Actuators, 2022
In this paper, a novel deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed to achieve the fixed point flight control of a quadrotor.
Wentao Xue, Hangxing Wu, Hui Ye, Shuyi Shao   +3 more
doaj   +3 more sources

Secondary Cooling Water System Control Method Based on Deep Reinforcement Learning [PDF]

Sensors
The secondary cooling water system is difficult to control because of loop coupling, thermal inertia, and strict actuator constraints. In addition, when conventional proximal policy optimization (PPO) uses Gaussian action sampling with clipping, the ...
Jin Xu, Yu Cheng, Cheng Shen, Qingxin Zhang   +3 more
doaj   +2 more sources

A CCO–PPO Framework for Autonomous UAV Trajectory Tracking in Complex and Disturbed Environments [PDF]

Sensors
Accurate trajectory tracking is fundamental to the autonomous operation of unmanned aerial vehicles (UAVs) in complex tasks. While proximal policy optimization (PPO) has shown strong potential in UAV control, its performance is highly sensitive to ...
Xize Guo, Chao Fan, Boxuan Shao, Qi Deng, Jiahao Chen, Tao Zhang, Wentao Zhang   +6 more
doaj   +2 more sources

Deep Reinforcement Learning for Collision-Aware Leader–Follower Formation Control [PDF]

Sensors
This work tackles leader–follower navigation in scenarios where rapid, unpredictable leader maneuvers can trigger unsafe proximity or collisions. We apply Proximal Policy Optimization (PPO) to learn adaptive follower behavior capable of tracking the ...
Sławomir Romaniuk, Jakub Budnik
doaj   +2 more sources

TUC-PPO: Team Utility-Constrained Proximal Policy Optimization for spatial public goods games

Chaos, Solitons and Fractals
We introduce Team Utility-Constrained Proximal Policy Optimization (TUC-PPO), a new deep reinforcement learning framework. It extends Proximal Policy Optimization (PPO) by integrating team welfare objectives specifically for spatial public goods games. Unlike conventional approaches where cooperation emerges indirectly from individual rewards, TUC-PPO ...
Zhaoqilin Yang, Youliang Tian, Ruichen Zhang   +2 more
exaly   +4 more sources

PPO-ACT: Proximal policy optimization with adversarial curriculum transfer for spatial public goods games

Chaos, Solitons and Fractals
This study investigates cooperation evolution mechanisms in the spatial public goods game. A novel deep reinforcement learning framework, Proximal Policy Optimization with Adversarial Curriculum Transfer (PPO-ACT), is proposed to model agent strategy optimization in dynamic environments.
Zhaoqilin Yang, Youliang Tian, Chanchan Li   +2 more
exaly   +4 more sources