Adaptive neural control of PEMFC system based on data-driven and reinforcement learning approaches

2022 ◽  
Vol 120 ◽  
pp. 105022
Author(s):  
Christophe Lin-Kwong-Chon ◽  
Cédric Damour ◽  
Michel Benne ◽  
Jean-Jacques Amangoua Kadjo ◽  
Brigitte Grondin-Pérez
Keyword(s):  
Reinforcement Learning ◽  
Neural Control ◽  
Data Driven ◽  
Learning Approaches ◽  
Adaptive Neural Control

Adaptive neural control for a class of stochastic non-strict-feedback nonlinear systems with time-delay

2016 ◽  
Vol 214 ◽  
pp. 750-757 ◽  
Author(s):  
Yumei Sun ◽  
Bing Chen ◽  
Chong Lin ◽  
Honghong Wang
Keyword(s):  
Time Delay ◽  
Nonlinear Systems ◽  
Neural Control ◽  
Adaptive Neural Control ◽  
Strict Feedback

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

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