Metadata-Version: 1.2
Name: RL_for_reco
Version: 1.0.23
Summary: A Python toolkit of Deep Reinforcement Learning for Structured Data-Oriented Recommendation.
Home-page: https://github.com/gowun/RL_for_reco.git
Author: Gowun Jeong
Author-email: gowun.jeong@gmail.com
License: MIT
Description: # Deep Reinforcement Learning for Business Structured Data
        ---
        
        ## Item_Reco
        ---
        
        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.
        
        
        
        ## Network_for_Reco
        ---
        
        A class to update Q-values though a nueral network.
        This is also a general form avaiable to any problem.
        
        
        ## RL_Learn
        ---
        
        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. 
        
        
        ## TorchModel
        ---
        
        Several classes to build a neural network by pyTorch.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
