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Deep Reinforcement with Pytorch

Reinforcement Learning is a type of machine learning that allows machines and software agents to act smart and automatically detect the ideal behavior within a specific environment, in order to maximize its performance and productivity.

Reinforcement Learning is becoming popular because it not only serves as an way to study how machine and software agents learn to act, it is also been used as a tool for constructing autonomous systems that improve themselves with experience.

Pytorch is one of the most versatile Deep Learning to implement deep reinforcement learning. In this course, you will learn how to use Pytorch for reinforcement learning..

Course Highlights

By end of the course, learners will 

  • Understand the fundamental concepts of Q Values and Q Tables 
  • Code Q Learning and SARSA 
  • Use OpenAI Gym
  • Code Deep Q Network
  • Code Policy Gradient 


HRDF SBL Claimable for Employers Registered with HRDF

HRDF claimable

Course Code: M1046

Course Booking


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Course Cancellation/Reschedule Policy

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
Note the venue of the training is subject to changes due to class size and availability of the classroom.
Note the minimal class size to start a class is 3 Pax.

Course Details

Module 1 Introduction to Reinforcement Learning

  • What is Reinforcement Learning (RL)
  • Applications of RL
  • Basic Concepts of RL
  • RL Methods
  • Key RL Algorithms

Module 2 Q Learning

  • Q Value and Q-Table
  • Q-Learning and Bellman Equation
  • Q-Learning Algorithm
  • Epsilon Greedy Explore-Exploit Strategy
  • Implementation of Q-Learning in Python
  • Max Q-Value Policy

Module 3 SARSA

  • On-Policy vs Off-Policy Learning
  • What is SARSA?
  • SARSA Value Update
  • Sarsa Algorithm
  • Implementation of SARSA in Python

Module 4 OpenAI Gym

  • What is OpenAI Gym
  • Install OpenAI Gym
  • OpenAI Gym Operations
  • Q-Learning on OpenAI Gym
  • SARSA on OpenAI Gym

Module 5 Deep Q-Network (DQN)

  • Why Deep Q-Learning?
  • Challenges of Implementing Deep Q-Learning
  • Target Network & Experience Replay
  • What is Deep Q Network (DQN)?
  • DQN Algorithm
  • Implementing DQN on OpenAI Gym with Keras

Module 6 Policy Gradient

  • Limitation of DQN
  • Policy Based Methods
  • Policy Gradient Theorem
  • REINFORCE Algorithm
  • Implementing PG on OpenAI Gym with Keras

Course Admin

Basic knowledge of Python and Raspberry Pi are assumed.

Who Should Attend

  • Engineers
  • Computer Vision Engineers
  • Scientists


Data Science TrainerDr. Aanand is a Full Stack Data Scientist who once had a torrid love affair with Physics. He has consulted and published in the area of Public Health, Electricity Markets, Telecom, BFSI, Advertising & Communication Strategies and Digital & Social Media Technologies. He has worked on assignments with international agencies such as International Monetary Fund, World Bank, Royal Netherland Embassy etc. besides MNCs like Tata Consultancy Services, Kie Square Consulting and several government organizations of national importance.

He regularly conducts general training programs in Python (Pandas, NumPy, SciPy, Matplotlib, Bokeh), R (dplyr, rstanarm, knitR, ggplot2), Data Visualization (Tableau, D3.js) and Machine Learnng (Reinforced Learning, Scikit Learn) and specialized training programs on Structural Equation Modeling and SAP Hana.

He holds a doctorate in Operations Research from Indian Institute of Management Ahmedabad and a post graduate in Physics from University of Mumbai. He has advanced training in mathematical programming including optimization, advanced multivariate data analysis, and simulation techniques. When he is not teaching or consulting he can be found meditating or heading for an adventurous trek.

TensorFlow TrainerDr. Nouar Dah is an experienced Lab Instructor at YPU. He has experience has a software engineer and senior hardware engineer. He has a PhD in Mechatronics Engineering from IIUM. He is well-versed in microcontrollers, FPGA, Matlab and VHDL.

Machine Learning TrainerAmir Othman is a software engineer by profession. Being educated in Bauhaus Universität Weimar and Hochschule Ulm, he brings experiences from different facades of the world.

With expertise in web technology, natural language processing and machine learning, he is a freelance data scientist. Some of his works include two international news aggregator and

He also holds an impressive port folio for data visualizations, primarily focusing on web based techniques.

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