Mastering the Dyna Q Learning method by solving Maze Problem
Welcome to the "Reinforcement Learning using Python" series! In this episode, we tackle the Simple Maze problem. You'll learn how to implement the Dyna Q Learning algorithm from scratch using just Python to find the optimal policy and navigate the agent to the goal state.
PERFECT FOR:
- Anyone new to Reinforcement Learning (RL)
- Students and engineers looking for a practical, code-first introduction to Value Iteration
- Data Scientists building foundational RL knowledge
TECH STACK:
- Python
- NumPy (for fast array operations)
RESOURCES: Code on Google Colab: https://colab.research.google.com/drive/1tKgC4RhLht2mIYi6AgXV6H7OY_1IK2dk#scrollTo=g1JvPd3hYT2-&line=5&uniqifier=1
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