Reinforcement Learning Based Latency Minimization in Secure NOMA-MEC Systems With Hybrid SIC

Reinforcement Learning Based PID Control of Wind Energy Conversion Systems

In this paper an adaptive PID controller for Wind Energy Conversion Systems (WECS) has been developed. Theadaptation technique applied to this controller is based on Reinforcement Learning (RL) theory. Nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability an...

reinforcement learning based pid control of wind energy conversion systems

in this paper an adaptive pid controller for wind energy conversion systems (wecs) has been developed. theadaptation technique applied to this controller is based on reinforcement learning (rl) theory. nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability an...

Latency Minimization of Order-Preserving Distributed Event-Based Systems

Nowadays sensors are increasingly deployed in many kinds of applications and deliver streams of data that they continuously collect. This data is significant as it provides important information in real time. However, without a fast processing of this information the sensors only provide a pointless stream of data. Hence, there is a need for automatic processing to extract meaningful informatio...

Inverse Reinforcement Learning through Policy Gradient Minimization

Inverse Reinforcement Learning (IRL) deals with the problem of recovering the reward function optimized by an expert given a set of demonstrations of the expert’s policy. Most IRL algorithms need to repeatedly compute the optimal policy for different reward functions. This paper proposes a new IRL approach that allows to recover the reward function without the need of solving any “direct” RL pr...

Hierarchical Hybrid Reinforcement Learning Algorithms