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  • Deep Reinforcement Learning with Python

Deep Learning and Machine Learning on Raspberry Pi with Tensorflow

Course Information

  • Sessions 2 days
  • Duration 15 hrs
  • Level Intermediate
  • Assessment NA

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

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Certification

  • Certificate of Completion from Tertiary Infotech - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Infotech.

Deep Reinforcement Learning with Python

Course Code: C1045

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What's This Course About

Step into the cutting-edge domain of Deep Reinforcement Learning (DRL) with our tailored program at Tertiary Courses. Beginning with the foundational concepts of Markov Decision Process (MDP) and Reinforcement Learning (RL), we ensure a profound understanding, setting the stage for more advanced subjects. With hands-on exercises, participants will grasp RL dynamics using prominent tools like OpenAI Gym and Stable Baselines, paving the way to practical application and comprehension.

Venturing further, the course covers the intricacies of algorithms such as Q-Learning, DQN, Policy Gradient, A2C, A3C, and PPO. Custom Policy Networks on Stable Baselines enrich the learning experience, allowing participants to tweak and adapt as per specific requirements. Concluding with a brief insight into Model-based RL, this program encapsulates the broad spectrum of DRL, ensuring participants are well-equipped to handle real-world AI challenges.

Funding Options

No funding is available for this course

For WSQ funding, please checkout the details at WSQ - Reinforcement Learning Course

Course Fee

$600.00 (GST-exclusive)
$654.00 (GST-inclusive)

Course Date

Course Time

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Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Cancellation & Reschedule Policy

  • You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
  • Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Course Details

What You'll Learn

Topic 1 Introduction to Reinforcement Learning

  • Fundamental Concepts of Reinforcement Learning (RL)
  • Types of RL Algorithms
  • Applications of RL
  • Markov Decision Process

Topic 2 OpenAI Gym and Stable Baselines

  • Introduction to OpenAI Gym
  • Install OpenAI Gym and Stable Baselines
  • Create Agent and Policy on Gym

Topic 3 Value Based Q-Learning

  • Overview of Value Based Learning
  • Value Functions and Bellman's Equations
  • Exploration Strategies
  • Q-Learning Algorithm
  • SARSA Algorithm
  • Deep Q Network (DQN) Algorithm

Topic 4 Policy Based Learning

  • Overview of Policy Based Learning
  • Policy Network
  • Policy Gradient Algorithm

Topic 5 Advanced RL Algorithms

  • Actor-Critic A2C/A3C Algorithms
  • Proximal Policy Gradient (PPO/PPO2)

Topic 6 Advanced Stable Baselines Techniques

  • Create Custom Policy Networks
  • Callbacks and Tensorboard

Topic 7 Brief Introduction to Model-Based Learning

  • Introduction to Model-Based Learnings
  • Brief Overview of AlphaZero
  • Model Predictive Control

Course Info

Prerequisite

This is an intermediate course. The following knowledge is assumed:

  • Basic Python

Software Requirement

Please install the following software prior to the class

1. Pycharm : - Install Pycharm (https://www.jetbrains.com/pycharm/download/)

2 . Install Tensorflow on Mac

Please follow this guide to install Tensorflow on Mac https://www.tensorflow.org/install/install_mac

Alternatively, you can enter the following commands on your Mac terminal

pip3 install tensorflow

3 . Install Tensorflow on Window

Please follow this guide to install Tensorflow on Window https://www.tensorflow.org/install/install_windows

Job Roles

Job Roles

  • Machine Learning Engineer
  • Robotics Engineer
  • Game Developer (AI-focused)
  • AI Research Scientist
  • Data Scientist (branching into RL)
  • Autonomous Systems Developer
  • Simulation Engineer (using RL)
  • Optimization Specialist
  • AI Product Manager (oversight on RL projects)
  • Control Systems Engineer (using RL)
  • Finance Quant (using RL for trading strategies)
  • NLP Engineer (using RL for certain applications)
  • Recommendation System Developer (using RL)
  • AI Solutions Architect
  • Drone Algorithm Developer.

Trainers

Trainers

Solomon Soh Zhe Hong: Solomon is ACTA certified and has trained and coached over 100 professionals in the area of data science, python programming and coding. Solomon is a Certified AI Engineer Associate by AI Singapore and holds certifications in Alibaba Cloud Architect and Alteryx respectively. Solomon interests include Reinforcement Learning, Natural Language Processing and Time-Series analysis. Marcel Leng: Marcel Leng is a ACTA certified. Marcel graduated with majors in Applied Mathematics and Physics from the National University of Singapore. His core specialisation skills are R, Python, Machine Learning, Statistical Analysis, and Data Visualisation in Tableau. His current interests include Machine Learning, Deep Learning, Artificial Intelligence, Internet of Things, Robotics and Programming.

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