Reinforcement Learning: Essentials

Reinforcement Learning: Essentials

• define reinforcement learning and describe its essential elements
• recognize the key differences between the reinforcement learning and machine learning paradigms
• depict the flow of reinforcement learning using agent, action, and environment
• describe different state change scenarios and transition processes in reinforcement learning
• recognize the role of rewards in reinforcement learning
• list the essential steps agents take to make decisions in reinforcement learning
• recognize prominent reinforcement learning environment types
• install OpenAI Gym and OpenAI Universe
• list reinforcement learning elements, agents involved in the process and the steps they take, and reinforcement learning environments

Explore reinforcement learning and its components, which can be used to help develop critical algorithms for decision making.

Reinforcement Learning: Tools & Frameworks

Reinforcement Learning: Tools & Frameworks

• recognize the different types of reinforcement learning that can be implemented for decision-making
• implement reinforcement learning using Keras and Python
• identify the role of the Markov decision process in reinforcement learning
• describe Q-learning, Q-learning rule, and deep Q-learning
• install TensorFlow
• implement reinforcement learning using TensorFlow
• implement Q-learning using Python
• implement reinforcement learning using Python and TensorFlow and implement Q-learning using Python

Discover reinforcement learning types and how to implement reinforcement learning using Keras, Python, and TensorFlow. Explore the concept of Q-learning and how to implement it using Python.