… 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. …

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 …

… (IPC), methods originating from the reinforcement learning literature have entered the … of
what constitute the main components of any reinforcement learning method, and it provides the …

… learning systems, including new learning methods for artificial neural networks, most of these
systems learn … — lead to learning methods for networks to which we now turn our attention. …

… We try to solve the MAB problem with standard reinforcement learning methods. The action
a of the agent is to choose which arm to pull. A reward will be given right after the action is …

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

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

… These considerations provide the basis for powerful methods of reinforcement learning. It
is acknowledged that not all environments fit comfortably into the essentially finite-automation …

… we consider to be a reinforcement learning method. … Most of the reinforcement learning
methods we consider in this … this to solve reinforcement learning problems. For example, solution …

… ) methods that are frequently used algorithms in reinforcement learning [36] which follows a
class of policy-based methods… The method is to find a neural network parameterized policy in …