… scheme, usually for supervised or unsupervised learning, and can be integrated with
reinforcement learning, usually as a function approximator. Supervised and unsupervised …

… decision-making model. This paper uses two deep reinforcement learning algorithms for the
… In the upper layer model, we use the D3QN algorithm to distinguish the potential value of …

… Abstract—This paper utilizes a fully model-free and data-driven deep reinforcement learning
(DRL) framework to develop an intelligent controller that can exploit information to optimally …

… The integration of IoT and ACS results in a new concept - autonomous IoT (AIoT). The …
intelligence, especially reinforcement learning (RL) and deep reinforcement learning (DRL) for …

… However, pure learning-based approaches lack the hardcoded safety measures of model-based
controllers. Here we propose a hybrid approach for integrating a path planning pipe …

… are limited by the accuracy of forecasting or model. A novel model-free dynamic dispatch
strategy for IES based on improved deep reinforcement learning (DRL) is proposed to solve the …

… deep RL and traditional machine learning are huge. The current mainstream machine learning
… the unknown domain and enables integration with other methods such as TRPO. Similar …

… models functioning at the level of basic actions. In this work, we propose a framework that
integrates deep reinforcement learning … deep reinforcement learning in complex environments. …

… to integrate the recent advances in attention models in … deep reinforcement learning and
2) introducing a framework for endend autonomous driving using deep reinforcement learning …

… -learning integration, … how to integrate planning in the learning and acting loop. After these
two sections, we also discuss implicit model-based RL as an end-to-end alternative for model …