A class to recommend products to customers with their any current information and product-recommended history. Class variable items indicates the products as well as their associate promotions, offers such as any recommendation type. If you want to take a case where customers have not recommendation, you can use 'none' to represent the case. States, actions and reward are respectively n-dim array, 1-d array and a float number. A transition model, state + action => (state, reward), is assumed as a multi-output neural network on TorchModel.
This framework, actually, is applicable to problems of any structured data.
A class to update Q-values though a nueral network. This is also a general form avaiable to any problem.
A class to formulate a Deep Q Learning problem(an environment, an agent and its policy and associated parameters) and to learn the agent by a Deep Q Network and its approximator.
Several classes to build a neural network by pyTorch.