Foundational Courses
Reinforcement Learning (RL) Specialist
To introduce learners to the fundamentals and advanced techniques
5
44 enrolled students
Objective
To introduce learners to the fundamentals and advanced techniques of reinforcement learning, guiding them in building RL agents and applying them in practical environments such as games and robotics.
Basic To Advance
You will progress through this course from basics to advanced level.
Duration
3 Months
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Modules
Module 1: Introduction to Reinforcement Learning
Objective:
Introduce the basic concepts of reinforcement learning, including the agent-environment setup, rewards, and foundational algorithms like Q-learning and Markov Decision Processes (MDP).
Topics:
- Basics of RL: agent, environment, rewards
- Markov Decision Process (MDP), Q-learning
- Practical: Implement Q-learning on a grid-world environment
Hands-on Exercise:
Implement Q-Learning on a Grid-World Environment
Create a simple 5×5 grid-world environment where an agent can move up, down, left, and right. The agent should start at a random position and move to reach a goal position while avoiding obstacles. Implement the Q-learning algorithm to allow the agent to learn the optimal policy for navigating the grid. Visualize the agent’s movement and the learned Q-values on the grid.
Module 2: Deep Q-Networks (DQN)
Objective:
Dive into deep Q-networks, focusing on techniques like experience replay and target networks, to enhance the stability and efficiency of Q-learning in more complex environments.
Topics Covered:
- Experience replay
- Target networks
- Practical: Build a DQN agent to play a simple game (e.g., CartPole)
Hands-on Exercise:
Build a DQN Agent to Play CartPole
Use the OpenAI Gym CartPole environment and implement a Deep Q-Network (DQN). Include key DQN components like experience replay and target networks. Train the agent to balance the pole for as long as possible. Monitor and visualize the agent’s performance over episodes, tracking the cumulative reward and loss.
Module 3: Policy Gradient Methods
Objective:
Introduce policy gradient techniques, including the REINFORCE algorithm and Actor-Critic methods, for tasks requiring continuous action spaces.
Topics Covered:
- REINFORCE algorithm, Actor-Critic methods
- Practical: Train a policy gradient agent on a continuous action space task
Hands-on Exercise:
Train a Policy Gradient Agent on a Continuous Action Task
Implement the REINFORCE algorithm (or a basic Actor-Critic method) to solve a continuous action task, such as the MountainCarContinuous or Pendulum environment in OpenAI Gym. Use a neural network to approximate the policy, and visualize the agent’s performance over time. Compare the performance of REINFORCE versus a basic Q-learning agent on the same task.
Module 4: Advanced Topics in RL
Objective:
Explore advanced reinforcement learning techniques, such as PPO, A3C, multi-agent reinforcement learning, and reward shaping, for complex environments.
Topics:
- PPO, A3C,
- multi-agent RL,
- reward shaping
Hands-on Exercise:
Implement PPO for a Complex Environment (e.g., Atari Game or Robotic Control)
Use Proximal Policy Optimization (PPO) to train an agent in a complex environment, such as the Atari game “Pong” or a simulated robotic environment (e.g., Fetch from OpenAI Gym). Implement PPO’s clipped objective function and reward normalization. Visualize the agent’s performance and compare it with simpler RL methods like DQN. Optionally, experiment with reward shaping to improve convergence.
Module 5: Capstone Project
Objective:
Apply all learned techniques in a real-world RL project, such as optimizing a robotic control system or an inventory management scenario.
Project:
Create an RL agent to solve a real-world problem, such as robotic control optimization or inventory management.
Hands-on Exercise:
- Robotic Control Optimization:
- Inventory Management:
- Autonomous Driving (optional):
Frequently Asked Questions
1. What is the Reinforcement Learning (RL) Specialist course about?
This course dives into reinforcement learning (RL), a type of machine learning where agents learn how to make decisions by interacting with an environment. You will learn key concepts like reward functions, Markov decision processes (MDPs), policy optimization, Q-learning, and deep reinforcement learning (DRL).
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