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Deep Reinforcement Learning with Python

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.

Additional Information

Duration

2 months (Full Time)

Assessment

3 hours online assessment after each module

Class (No of teacher : student): 1:20

Intake

  • 3 Nov 2025 to 29 Sep 2026
  • 4 May 2026 to 26 June 2026
  • 2 Jan 2026 to 2 Mar 2026
  • 2 Mar 2026 to 27 Apr 2026

Enrolment Requirement

  • Age: 21 years old and above
  • Language Proficiency: At least C6 for GCE "O" Level English
  • Academic: At least C6 for GCE "O" Level in any 3 subjects

Graduation Requirement

  • Attendance: 75%
  • Assessment: Passed

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.

WSQ Funding

Full Fee $600.00 Before GST
GST $54.00 9% of fee
Baseline Nett $354.00 SG/PR age 21+ · 50% funded
MCES / SME Nett $234.00 SG age 40+ · 70% funded
Funding and Grant Applications

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

Dr Alvin Ang: Dr Alvin Ang is a ACTA certified trainer. Alvin Ang did his Ph.D., Masters and Bachelors from NTU, Singapore. Previously he was a Principal Consultant (Data Science) as well as an Assistant Professor. He was also 8 years SUSS adjunct lecturer. His focus and interest is in the area of real world data science. Though an operational researcher by study, his passion for practical applications outweigh his academic background. He owns a startup externally Terence Ee: Terence Ee is a ACTA certified trainr that has delivered IT training in Singapore and Myanmar. He has also facilitated faith formation courses for Christians in Singapore and Myanmar. As a trainer, his mission is to co-create insightful and actionable learning experiences with his learners.His current areas of focus include project management, information security management, quality management and office productivity applications. Terence has more than 25 years of corporate IT experience. He has held senior management roles in the public and private sectors. He holds a Master of Science in Technology Management, a Bachelor of Science in Computer and Information Sciences, a Diploma in Family Education, and the Advanced Certificate in Training and Assessment (ACTA). Part of his spare time goes towards tutoring his children in their studies (while learning a thing or two along the way). He is also imparting to them the essential skills for thriving in a digital world.  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.

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