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Course Description
Reinforcement learning is a self-teaching machine training method. Unlike supervised learning, the training data doesn’t come with an answer key; nor is the dataset properly labeled, as it is in unsupervised learning. Instead, reinforcement learning learns by trial and error—after each decision, the algorithm learns whether its choice was correct, neutral, or incorrect. This technique is ideal for training automated systems to make small decisions without any human guidance.
In this KnowledgeCity course on Python for Data Science Advanced: Reinforcement Learning, you’ll learn about reinforcement learning techniques that apply to machine learning. We’ll introduce you to OpenAI Gym, a machine learning subfield that focuses on getting an agent to learn optimal decision-making strategies by interacting with an environment. The agent takes actions in a specific state to achieve a goal while receiving feedback in the form of rewards. We’ll also explore Q-Learning, a popular reinforcement learning algorithm used to train agents to make sequential decisions in an environment to maximize the cumulative reward. Then we’ll introduce you to Deep Q-Networks, a class of reinforcement learning algorithms that combine the power of deep neural networks with Q-Learning.
What You'll Learn
- Understand the principles of reinforcement learning as a trial-and-error, self-teaching machine training method
- Explain how to perform environmental testing using OpenAI Gym
- Apply Q-Learning to train agents to make sequential decisions that maximize cumulative reward
- Describe how to use deep neural networks to reinforce Q-Learning through Deep Q-Networks
Key Takeaways
- Reinforcement learning is a self-teaching method that learns by trial and error, without an answer key or labeled dataset, judging each decision as correct, neutral, or incorrect.
- It is ideal for training automated systems to make small decisions without human guidance.
- OpenAI Gym focuses on getting an agent to learn optimal decision-making strategies by interacting with an environment, taking actions in a state to achieve a goal while receiving rewards as feedback.
- Q-Learning is a popular reinforcement learning algorithm used to train agents to make sequential decisions that maximize cumulative reward.
- Deep Q-Networks combine the power of deep neural networks with Q-Learning.
Frequently Asked Questions
What topics does this course cover?
This advanced course covers reinforcement learning techniques applied to machine learning, including OpenAI Gym, Q-Learning, and Deep Q-Networks.
What is reinforcement learning?
Reinforcement learning is a self-teaching machine training method that learns by trial and error. Unlike supervised learning, the training data has no answer key, and unlike unsupervised learning, the dataset is not labeled; instead, after each decision the algorithm learns whether its choice was correct, neutral, or incorrect.
What is OpenAI Gym in this course?
OpenAI Gym is a machine learning subfield focused on getting an agent to learn optimal decision-making strategies by interacting with an environment, where the agent takes actions in a specific state to achieve a goal while receiving feedback in the form of rewards.
What skills will I gain from this course?
You will gain skills in Deep Learning, OpenAI Gym, and Reinforcement Learning.
What is the difference between Q-Learning and Deep Q-Networks?
Q-Learning is a reinforcement learning algorithm used to train agents to make sequential decisions that maximize cumulative reward, while Deep Q-Networks are a class of reinforcement learning algorithms that combine deep neural networks with Q-Learning.
