Reinforcement Learning

Dive into an exciting journey into the world of Reinforcement Learning (RL) with our comprehensive course. This course covers foundational concepts to advanced techniques, providing you with the skills to develop and apply RL algorithms to solve real-world problems.

DIFFICULTY

Beginner

COURSE TYPE

SCHEDULE

Self-paced

PRE-REQUISITES

No prior experience with AI or machine learning required

What you'll learn

Fundamentals of RL

Understanding the RL framework (agents, states, actions, rewards)

Value-Based Methods

Dive into the mathematics of the Bellman equation and see how it helps estimate the value of different actions. Practice implementing algorithms like Q-Learning and SARSA, and learn how to represent an agent’s knowledge in Q-tables.

Policy-Based Methods

Instead of focusing on action values, directly adjust the probabilities of choosing certain actions. Explore policy gradients, such as REINFORCE, which work well in continuous or complex action spaces where directly calculating action values is difficult.

Deep Reinforcement Learning:

Combine RL with the power of neural networks to tackle more complex tasks. Learn about deep Q-networks (DQN), how neural networks approximate value and policy functions, and techniques to stabilize training for improved performance.

Advanced Topics:

Venture beyond the basics into model-based RL, where agents learn an internal model of the environment and plan ahead. Discover multi-agent RL to handle scenarios with multiple interacting agents, and explore transfer and meta-learning to quickly adapt to new tasks and challenges.

Grid World Navigation

Implement a simple agent-environment setup in a 2D grid world. Visualize the agent’s random movements and learn how to represent states, actions, and rewards.

Q-Learning
Implementation

Implement the Q-learning algorithm to update Q-values from interactions with the environment. Visualize the agent’s improvement over time and examine the learned Q-values for each state.

Policy Gradients

Use a policy-based method to train an agent. Implement REINFORCE and watch as the agent’s policy probabilities evolve, showing increased preference for actions leading to higher rewards.

Maze Navigation

Combine RL with deep learning to solve a more complex task—a maze navigation problem. Train a neural network to approximate Q-values, observe the agent’s trajectory towards the goal, and visualize the learning process through changing rewards and losses per episode.

Write your awesome label here.

Course Lessons

Frequently Asked Questions

How do I choose between a value-based method (like Q-learning) and a policy-based method (like REINFORCE)?

Value-based methods are great for discrete action spaces and when estimating action-values is straightforward. Policy-based methods shine in continuous or complex action spaces where directly learning a policy may be more efficient.

What is the role of neural networks in RL?

Neural networks function as powerful approximators for value functions or policies, enabling agents to handle more complex, high-dimensional states than simple tabular methods would allow.

How do I handle the exploration vs. exploitation trade-off?

Techniques like epsilon-greedy strategies or decaying exploration parameters allow agents to explore enough early on and gradually exploit known good actions as they gain more experience.

Why does training sometimes become unstable or show highly variable results?

RL agents learn from correlated data that changes as they interact with the environment. Stabilization techniques include experience replay, target networks, and carefully tuning hyperparameters.

Can RL be applied to real-world industries and domains?

Absolutely. RL’s applications range from robotics and autonomous vehicles to finance, healthcare, and intelligent gaming agents. The methods and principles taught here serve as a foundation for tackling complex, real-world challenges.