Deep Reinforcement Learning for Control and Correction
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* Overview

For control and correction of manufacturing systems the library implements the **Object Shape Error Correction (OSEC)** methodology using Deep Deterministic Policy Gradients (DDPG). The methodology aims to correct root causes in manufacturing system considering a variety of system costs and constraints. Notebooks for implementation of DDPG using a custom made assembly Matlab environment and TensorFlow based DDPG agent and learning is located within `Deep Reinforcement Learning Module <https://github.com/sumitsinha/Deep_Learning_for_Manufacturing/tree/master/deep_reinforcement_learning>`_ of the library 


* `Matlab Python Linking code <https://github.com/sumitsinha/Deep_Learning_for_Manufacturing/blob/master/cae_simulations/cae_simulations.py>`_ - Enables low latency integration between Multi-physics environments (Computer Aided Engineering (CAE)) in Matlab and TensorFlow based deep learning agents


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.. figure:: osec.png
   :align:   center

   *Fig 1: Object Shape Error Correction Framework*


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.. figure:: osec_obj.png
   :align:   center

   *Fig 1: Object Shape Error Correction Objective*