Resources: Reinforcement Learning

Exercises: Reinforcement Learning

Resources: Capstone Project

Exercises: Capstone Project

Resources: Transformers and Mechanistic Interpretability

Exercises: Transformers and Mechanistic Interpretability

Resources: Fundamentals

Exercises: Fundamentals

Resources: Prerequisites

Exercises: Prerequisites

Reinforcement learning is an important field of machine learning. It works by teaching agents to take actions in an environment to maximise their accumulated reward.
In this chapter, you will be learning about some of the fundamentals of RL, and working with OpenAI’s Gym environment to run your own experiments.

Each exercise will have a difficulty and importance rating out of 5, as well as an estimated maximum time you should spend on these exercises and sometimes a short annotation. You should interpret the ratings & time estimates relatively (e.g. if you find yourself spending about 50% longer on the exercises than the time estimates, adjust accordingly). Please do skip exercises / look at solutions if you don't feel like they're important enough to be worth doing, and you'd rather get to the good stuff!

All of the material needed to complete these exercises, including readings, instructions, and hints, are available on this section's streamlit page. Please follow along with the streamlit page to complete the exercises, then submit your work here. 

By the end of the unit, you should be able to:
At the end of this week, you will have learned to:
\- Build your own agent to play the multi-armed bandit problem, implement methods from Sutton & Bardo
\- Implement a Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) to play the CartPole game
\- Apply RLHF to autoregressive transformers like the ones you built in the previous chapter


Submit a link to your exercises, through a GitHub repo, or your Collab notebooks, or through any other form you'd like. 


ARENA

Exercises: Reinforcement Learning

Exercises