Tutorial 4: From Reinforcement Learning to Planning
Tutorial 4: From Reinforcement Learning to Planning
In this tutorial, you will implement one of the simplest model-based reinforcement learning algorithms, Dyna-Q. You will understand what a world model is, how it can improve the agent's policy, and the situations in which model-based algorithms are more advantageous than their model-free counterparts.
Topics covered in this lesson
- Implementing a model-based RL agent, Dyna-Q, that can solve a simple task
- Investigating the effect of planning on the agent's behavior
- Comparing the behaviors of a model-based and model-free agent in light of an environmental change
External Links
Prerequisites
Experience with Python Programming Language
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