Deep Reinforcement Learning with Python

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

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

• 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.

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.