Month 9: Reinforcement Learning and Robotics

Month 9: Reinforcement Learning and Robotics

Week 1: Markov Decision Processes

• Day 1: Introduction to Reinforcement Learning
• Day 2: Understanding Markov Decision Processes: States, Actions, Transitions, Rewards
• Day 3: Policies and Value Functions
• Day 4: Bellman Equations and Dynamic Programming
• Day 5: Practical application and a mini-project

Week 2: Q-Learning and Deep Q-Networks (DQN)

• Day 1: Introduction to Q-Learning: Exploration vs Exploitation
• Day 2: Understanding the Q-Learning Algorithm
• Day 3: Deep Q-Networks (DQN): Integration of Deep Learning with Q-Learning
• Day 4: Improving DQN: Double DQN, Dueling DQN
• Day 5: Practical application and a mini-project

Week 3: Policy Gradient Methods

• Day 1: Introduction to Policy Gradient Methods
• Day 2: Understanding Policy Gradient Theorem
• Day 3: REINFORCE Algorithm
• Day 4: Actor-Critic Methods
• Day 5: Practical application and a mini-project

Week 4: Robotics and Control

• Day 1: Introduction to Robotics and Control
• Day 2: Understanding the Robot Operating System (ROS)
• Day 3: Robot Perception: Sensors and Vision
• Day 4: Robot Control: Motion Planning, PID Controllers
• Day 5: Reinforcement Learning for Robotics