Deep learning and reinforcement learning … Deep Deterministic Policy Gradient algorithm (DDPG),
a model-free Q-learning based method, which make it significantly more data-efficient …

… an efficient algorithm to solve the complicated multiple intersections control problems
whose state-action spaces are vast. To solve this problem, we propose a Deep Reinforcement …

… Deep reinforcement learning (DRL) has gained great success by learning directly from high-…
DRL and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can …

… The superior sample efficiency of our proposed method is … , “Reinforced imitation: Sample
efficient deep reinforcement … policy maximum entropy deep reinforcement learning with a …

… Our goal is to improve the sample efficiency of deep reinforcement learning by making a
simple yet effective modification. Without a single change of the network structure, training …

… Burgard, “Deep reinforcement learning with successor features … in indoor scenes using deep
reinforcement learning,” in IEEE … aware motion planning with deep reinforcement learning,” …

… In this work, we present a method to enhance the sample efficiency of deep reinforcement
learning algorithms. As such, it is agnostic to the applications, and per se does not raise any …

… , etc. on the learning dynamics of deep RL algorithms, using tools from deep learning
theory, is likely to be key towards developing robust and data-efficient deep RL algorithms. …

… deep reinforcement learning approaches to solve this problem. Particular attention is given
to actor-critic methods, off-policy reinforcement … of the art in deep learning approaches for …

… The focus of this work is sample-efficient deep reinforcement learning (… Our work can be
seen as an efficient approximate … Survey of deep reinforcement learning for motion planning of …