Build your ultimate AI agent
Course Description
Discover the World of Reinforcement Learning
Embark on an exhilarating exploration of Reinforcement Learning (RL), a pivotal branch of machine learning. This interactive course takes you on a comprehensive journey through the core principles of RL where you'll master the art of training intelligent agents, teaching them to make strategic decisions and maximize rewards.Master Essential Concepts and Tools
Your adventure starts with a deep dive into the unique aspects of RL. You'll not only learn foundational RL concepts but also apply key RL algorithms to practical scenarios using the renowned OpenAI Gym toolkit. This hands-on approach ensures a thorough grasp of RL essentials.Navigate Through Advanced Strategies and Applications
As your journey unfolds, you'll venture into the realms of advanced RL strategies to discover the intricacies of Monte Carlo methods, Temporal Difference Learning, and Q-Learning. By mastering these techniques in Python, you'll be adept at training agents for a variety of complex tasks.Transform Your Learning into Real-World Impact
Concluding this course, you'll emerge with a profound understanding of RL theory, equipped with the skills to apply it creatively in real-world contexts. You'll be ready to build RL models in Python, unlocking a world of possibilities in your projects and professional endeavors.Prerequisites
Curriculum
Course outline
1
Introduction to Reinforcement Learning
Dive into the exciting world of Reinforcement Learning (RL) by exploring its foundational concepts, roles, and applications. Navigate through the RL framework, uncovering the agent-environment interaction. You'll also learn how to use the Gymnasium library to create environments, visualize states, and perform actions, thus gaining a practical foundation in RL concepts and applications.
- Fundamentals of reinforcement learning50 XP
- What is Reinforcement Learning?50 XP
- RL vs. other ML sub-domains100 XP
- Scenarios for applying RL100 XP
- Navigating the RL framework50 XP
- RL interaction loop100 XP
- Episodic and continuous RL tasks100 XP
- Calculating discounted returns for agent strategies100 XP
- Interacting with Gymnasium environments50 XP
- Setting up a Mountain Car environment100 XP
- Visualizing the Mountain Car Environment100 XP
- Interacting with the Frozen Lake environment100 XP
2
Model-Based Learning
Delve deeper into the world of RL focusing on model-based learning. Unravel the complexities of Markov Decision Processes (MDPs), understanding their essential components. Enhance your skill set by learning about policies and value functions. Gain expertise in policy optimization with policy iteration and value Iteration techniques.
3
Model-Free Learning
Embark on a journey through the dynamic realm of Model-Free Learning in RL. Get introduced to to the foundational Monte Carlo methods, and apply first-visit and every-visit Monte Carlo prediction algorithms. Transition into the world of Temporal Difference Learning, exploring the SARSA algorithm. Finally, dive into the depths of Q-Learning, and analyze its convergence in challenging environments.
4
Advanced Strategies in Model-Free RL
Dive into advanced strategies in Model-Free RL, focusing on enhancing decision-making algorithms. Learn about Expected SARSA for more accurate policy updates and Double Q-learning to mitigate overestimation bias. Explore the Exploration-Exploitation Tradeoff, mastering epsilon-greedy and epsilon-decay strategies for optimal action selection. Tackle the Multi-Armed Bandit Problem, applying strategies to solve decision-making challenges under uncertainty.
Reinforcement Learning with Gymnasium in Python
Course
Complete
