Reinforcement Learning based NLP

Memory Based Learning in NLP

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...

Operation Scheduling of MGs Based on Deep Reinforcement Learning Algorithm

: In this paper, the operation scheduling of Microgrids (MGs), including Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), is proposed using a Deep Reinforcement Learning (DRL) based approach. Due to the dynamic characteristic of the problem, it firstly is formulated as a Markov Decision Process (MDP). Next, Deep Deterministic Policy Gradient (DDPG) algorithm is presented t...

Reinforcement Learning: Model-based

Reinforcement learning (RL) refers to a wide range of dierent learning algorithms for improving a behavioral policy on the basis of numerical reward signals that serve as feedback. In its basic form, reinforcement learning bears striking resemblance to ‘operant conditioning’ in psychology and animal learning: actions that are rewarded tend to occur more frequently; actions that are punished ar...

Preference-based Reinforcement Learning

This paper investigates the problem of policy search based on the only expert’s preferences. Whereas reinforcement learning classically relies on a reward function, or exploits the expert’s demonstrations, preference-based policy learning (PPL) iteratively builds and optimizes a policy return estimate as follows: The learning agent demonstrates a few policies, is informed of the expert’s prefer...