• Home
  • Basic Reinforcement Learning with Python

Basic Reinforcement Learning with Python

Course Information

  • Sessions 1 day
  • Duration 7.5 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.

Download Course Brochure

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

Basic Reinforcement Learning with Python

Course Code: C625

Be the first to review this product

What's This Course About

Unlock the potential of Reinforcement Learning (RL) with our meticulously crafted course at Tertiary Courses. Gain a foundational grasp of the intricate concepts of Markov Decision Process (MDP) and Reinforcement Learning (RL), ensuring a robust grounding in this cutting-edge AI technology. Our course also offers hands-on experience, allowing participants to learn and apply RL using the renowned OpenAI Gym and Stable Baselines.

Venture deeper into the realms of Q-Learning, DQN, and Policy Gradient, with our interactive coding sessions, ensuring not just theoretical knowledge but practical expertise. By the end of the course, participants will be adept in crafting sophisticated RL solutions using Python, setting them on the path to AI excellence. Embrace the future of AI with our comprehensive Reinforcement Learning training.

WSQ Funding

Full Fee $350.00 Before GST
GST $31.50 9% of fee
Baseline Nett $206.50 SG/PR age 21+ · 50% funded
MCES / SME Nett $136.50 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

$350.00 (GST-exclusive)
$381.50 (GST-inclusive)

Course Date

Course Time

* Required Fields

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

Course Info

SSG Training Grant

SSG TG is $15 per pax. Net fee after SSG TG is $303.86. Absentee Payroll is not eligible.

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

Richard Wan

Richard Wan is an ACLP-certified lecturer and software consultant with over 40 years of experience in software and hardware development, spanning AI, computer vision, and machine learning. He began his programming career with 8-bit computing in the late 1970s and went on to earn his M.Sc. in Electrical Engineering (Computer Vision) from the University of Wisconsin–Madison. His professional contributions include co-founding multiple high-tech companies, pioneering digital publishing technologies, and leading AI-driven software development in healthcare, defense, and manufacturing.

Richard has taught a wide range of technical courses, including machine learning with Scikit-Learn, deep learning with TensorFlow and PyTorch, and computer vision with OpenCV. In predictive analytics, he emphasizes the use of PyTorch for building deep learning models that can forecast trends, detect anomalies, and classify outcomes. His teaching approach blends decades of hands-on development with structured, beginner-friendly instruction, equipping learners with practical skills to transform data into prediction.

Review

Write Your Own Review

You're reviewing: Basic Reinforcement Learning with Python