Reinforcement learning (RL) has achieved tremendous success in many complex decision
making tasks. When it comes to deploying RL in the real world, safety concerns are usually …

… While most Reinforcement Learning work utilizes temporal … Rlearning, is modelled after
the popular Q-learning algorithm [… Programming or Reinforcement Learning literature, the …

… approaches [9], reinforcement learning methods prevent suboptimal performance, … DL
methods, where DL is equipped with the vigorous function approximation, representation learning …

… of reinforcement learning from a computer-science perspective. It is written to be accessible
to researchers familiar with machine learning. … of current methods for reinforcement learning. …

… machine learning, deep learning and reinforcement learning. … We obtain deep reinforcement
learning (deep RL) methods … following components of reinforcement learning: value function…

… For us, this problem de nes the eld of reinforcement learning: any method that is … a
reinforcement learning method. Our objective in this chapter is to describe the reinforcement learning …

… In this paper, we propose a reinforcement learning framework to improve the performance
of relation reasoning in KGs. Specifically, we train a RL agent to find reasoning paths in the …

… This effort was originally motivated by the desire to apply reinforcement learning methods
to problems of adaptive control of queueing systems, and to the problem of adaptive routing in …

… PbRL aims at rendering reinforcement learning applicable to … -based formulations of
reinforcement learning and make … , such as inverse reinforcement learning or learning with advice. …

… Based on the principle of storing and reusing transition experiences, a model-free, neural
network based Reinforcement Learning algorithm is proposed. The method is evaluated on …