Model-based design of experiments for parameter precision: State of the art

2008 ◽  
Vol 63 (19) ◽  
pp. 4846-4872 ◽  
Author(s):  
Gaia Franceschini ◽  
Sandro Macchietto
Keyword(s):  
Design Of Experiments ◽  
State Of The Art ◽  
Model Based ◽  
Parameter Precision

Proximal policy optimization with model-based methods

2022 ◽  
pp. 1-12
Author(s):  
Shuailong Li ◽  
Wei Zhang ◽  
Huiwen Zhang ◽  
Xin Zhang ◽  
Yuquan Leng
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Transition Model ◽  
Practical Applications ◽  
Original Algorithm ◽  
Policy Performance ◽  
Model Based ◽  
Model Free ◽  
Future State ◽  
Policy Optimization

Model-free reinforcement learning methods have successfully been applied to practical applications such as decision-making problems in Atari games. However, these methods have inherent shortcomings, such as a high variance and low sample efficiency. To improve the policy performance and sample efficiency of model-free reinforcement learning, we propose proximal policy optimization with model-based methods (PPOMM), a fusion method of both model-based and model-free reinforcement learning. PPOMM not only considers the information of past experience but also the prediction information of the future state. PPOMM adds the information of the next state to the objective function of the proximal policy optimization (PPO) algorithm through a model-based method. This method uses two components to optimize the policy: the error of PPO and the error of model-based reinforcement learning. We use the latter to optimize a latent transition model and predict the information of the next state. For most games, this method outperforms the state-of-the-art PPO algorithm when we evaluate across 49 Atari games in the Arcade Learning Environment (ALE). The experimental results show that PPOMM performs better or the same as the original algorithm in 33 games.

scholarly journals Constrained model-based design of experiments for the identification of approximated models

2018 ◽  
Vol 51 (15) ◽  
pp. 515-520 ◽  
Author(s):  
Marco Quaglio ◽  
Eric S. Fraga ◽  
Federico Galvanin
Keyword(s):  
Design Of Experiments ◽  
Model Based ◽  
Constrained Model

Implementation and Application of Model-Based Design of Experiments in a Flowsheet Simulator

2019 ◽  
Vol 65 (3) ◽  
pp. 1135-1145 ◽  
Author(s):  
Norbert Asprion ◽  
Roger Böttcher ◽  
Jonas Mairhofer ◽  
Maria Yliruka ◽  
Johannes Höller ◽  
...  
Keyword(s):  
Design Of Experiments ◽  
Model Based
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