Deep reinforcement learning control of white-light continuum generation

Generation of a White-Light Super Continuum

Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks

Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. In terms of how to dynamically adjust traffic signals’ duration, existing works either split the traffic signal into equal duration or extra...

Paraphrase Generation with Deep Reinforcement Learning

Automatic generation of paraphrases for a given sentence is an important yet challenging task in natural language processing (NLP), and plays a key role in a number of applications such as question answering, information retrieval and dialogue. In this paper we present a deep reinforcement learning approach to paraphrase generation. Specifically, we propose a new model for the task, which consi...

Deep Reinforcement Learning for Dialogue Generation

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on ...

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...