3

I think you are looking for quantum machine learning (QML), which is a relatively new field that sits at the intersection of quantum computing and machine learning. If you are not familiar with quantum computing (QC) and you are interested in QML, I suggest that you follow this course by prof. Umesh Vazirani and read the book Quantum Computing for Computer ...


2

In reinforcement learning (RL), an immediate reward value must be returned after each action, alomng with the next state. This value can be zero though, which will have no direct impact on optimality or setting goals. Unless you are modifying the reward scheme to try and make an environment easier to learn (sometimes called reward shaping), then you should ...


1

Why do you want to think of these algorithms as agents? An agent is an abstract and higher-level concept than the concept of an algorithm, which is just a set of instructions. You could have two agents, one that is supposed to find the minimum spanning tree and another that is supposed to find the shortest path between a source and goal nodes. In both ...


1

It depends on whether the action is part of the input or output of a neural network estimating the Q-value(state, action). The network on the left has the state as input and outputs one scalar value for each of the categorical actions. It has the advantage of being easy to setup and only needs one network evaluation to predict the Q-value for all actions. ...


Only top voted, non community-wiki answers of a minimum length are eligible