This is the repository for The Reinforcement Learning Workshop, published by Packt. It contains all the supporting project files necessary to work through the course from start to finish.
To get started with the project files, you'll need to:
With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning’s core techniques and frameworks. Following a hands-on approach, it allows you to learn reinforcement learning at your own pace to develop your own intelligent applications with ease.
- Use OpenAI Gym as a framework to implement RL environments
- Find out how to define and implement reward function
- Explore Markov chain, Markov decision process, and the Bellman equation
- Distinguish between Dynamic Programming, Monte Carlo, and Temporal Difference Learning
- Understand the multi-armed bandit problem and explore various strategies to solve it
- Build a deep Q model network for playing the video game Breakout