Welcome to the exciting journey of Reinforcement Learning (RL) guided by the course presented by David Silver. Whether you are a beginner eager to explore or an experienced developer diving deeper into the subject, this guide will help you grasp the course contents and its implementation techniques effectively.
Course Overview
This repository features comprehensive notes alongside practical implementations of various algorithms discussed in Silver’s course. The implementation is based on the Keras framework (using TensorFlow backend) and the famous OpenAI Gym framework.
Course Syllabus Breakdown
- Week 1: Introduction to Reinforcement Learning
- Week 2: Markov Decision Processes
- Week 3: Planning by Dynamic Programming
- Week 4: Model-Free Prediction
- Week 5: Model-Free Control
- Week 6: Value Function Approximation
- Week 7: Policy Gradient Methods
- Week 8: Integrating Learning and Planning
- Week 9: Exploration and Exploitation
- Week 10: Case Study: RL in Classic Games
Setting Up Your Environment
Before diving into the implementation, ensure you have the following dependencies installed in your Python environment:
- TensorFlow
- Keras
- Gym
- Numpy
These can be easily installed using pip.
Understanding the Code: An Analogy
Let’s imagine the RL environment as a board game. Each time a player (the agent) takes a turn (action), they can either win points (rewards) or lose them based on their position on the board (state). The rules (policy) tell the player the best moves to maximize their scores from future turns. Just like strategic decision-making in games, we aim to maximize our total score through informed actions reflecting the understanding of the game’s dynamics (value function).
Troubleshooting
If you encounter issues or errors during the course or its implementation, consider the following:
- Ensure all dependencies are correctly installed and updated.
- Verify that you have the correct Python version compatible with the libraries employed.
- Consult the issues section in the related GitHub repository for common problems and solutions.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Contribution
We welcome contributions! Feel free to create a Pull Request if you want to add implementations of discussed algorithms in different frameworks like PyTorch or Caffe, or improve existing implementations. For those just starting out, you might find this guide helpful.
Final Thoughts
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.