Deep Reinforcement Learning

Mr. Sewak has been the Lead Data Scientist/Analytics Architect for a number of important international AI/DL/ML software and industry solutions and has also been involved in providing solutions and research for a series of cognitive features for IBM Watson Commerce. He has 14 years of experience working as a solutions architect using technologies like TensorFlow, Torch, Caffe, Theano, Keras, Open AI, SpaCy, Gensim, NLTK, Watson, SPSS, Spark, H2O, Kafka, ES, and others.

Introduction to Reinforcement Learning.- Mathematical and Algorithmic understanding of Reinforcement Learning.- Coding the Environment and MDP Solution.- Temporal Difference Learning, SARSA, and Q Learning.- Q Learning in Code.- Introduction to Deep Learning.- Implementation Resources.- Deep Q Network (DQN), Double DQN and Dueling DQN.- Double DQN in Code.- Policy-Based Reinforcement Learning Approaches.- Actor-Critic Models & the A3C.- A3C in Code.- Deterministic Policy Gradient and the DDPG.- DDPG in Code.