Welcome to our vibrant playground for Reinforcement Learning (RL) exploration! 🚀 This repository offers insightful implementations of two powerful RL methods: Value Iteration and Q-learning. By diving into these methods, you'll uncover the secrets of optimal decision-making in dynamic environments. 🌟
- Value Iteration: Delve into this classic method to compute state values and unravel its impact on decision-making.
- Q-learning: Explore the wonders of Q-learning, a fundamental RL technique, and visualize its distinct approach to optimal decision-making.
- Markov Decision Processes (MDPs) Explained: Understand the backbone of RL with comprehensive explanations of Markov decision processes, laying the groundwork for your RL journey.
-
Clone the Repository:
git clone https://github.com/DeepNets-US/Q-Learning.git -
Explore the Notebooks:
- Open the notebooks to witness visualizations showcasing the differences between Value Iteration and Q-learning.
- Access the notebooks directly on Kaggle for interactive exploration.
-
Play and Learn:
- Tinker with the code, adjust parameters, and observe how decisions change in different scenarios.
-
Contribute:
- Found a bug or have an enhancement in mind? Contribute by forking this repository and submitting a pull request!
Reach out and let's discuss the fascinating world of Reinforcement Learning! Connect with us on LinkedIn and Kaggle. We'd love to hear your insights and experiences!
Start exploring and enjoy the journey into the exciting realm of Reinforcement Learning! 🎉✨