… [3] by combining deeplearning and reinforcementlearning [2]. … the success in deep reinforcementlearning can be applied … are continuous and reinforcementlearning for continuous …
… These feature are exploited by various techniques starting from traditional statistical methods, passing by neural networks and deeplearning, to deepreinforcementlearning. …
Y Li - arXiv preprint arXiv:1701.07274, 2017 - arxiv.org
… achievements of deepreinforcement learning (RL). … learning, deeplearning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep …
… version AlphaGo Zero, is deepreinforcementlearning (DRL), which is an improved version of reinforcementlearning (RL) with the integration of deeplearning. Once well trained, an RL/…
… developments in reinforcementlearning and deeplearning to develop a novel adaptive, model-free controller for general discrete-time processes. The deepreinforcementlearning (…
Y Liu, Q Liu, H Zhao, Z Pan, C Liu - Proceedings of the AAAI conference on …, 2020 - aaai.org
… reinforcementlearningapproaches have difficulties in the choice of market features. Deep learning approaches … The combination of RL and DL, called deepreinforcementlearning (DRL…
… This work examines applying deepreinforcementlearning to a chemical production … the differing approaches. Results show that the reinforcementlearning method outperforms …
Z Wang, W Pan, H Li, X Wang, Q Zuo - Aerospace, 2022 - mdpi.com
… as the deepreinforcementlearning algorithm involved in the approaches summarized in … Deepreinforcementlearning and the algorithms used in the research involved in this review …
CH Liu, Z Chen, J Tang, J Xu… - IEEE Journal on Selected …, 2018 - ieeexplore.ieee.org
… Toward this end, we propose to leverage emerging deepreinforcementlearning (DRL) for UAV control and present a novel and highly energyefficient DRL-based method, which we call …