College-Level Reinforcement Learning : A Comprehensive Dive!

Learn Deep Reinforcement Learning from the ground up. With a special case study on RLHF & RLVR for LLM tuning

What you’ll learn
✓ Understand reinforcement learning (RL) from the ground up (Including relevant proofs and derivations)
✓ Understand model-based & model-free RL techniques
✓ Understand value-based and policy-gradient RL optimization techniques
✓ Understand how to use deep learning in combination with reinforcement learning
✓ Understand RL techniques for discrete and continuous action control
✓ Understand Reinforcement Learning From Human Feedback (RLHF) & From Verifiable Rewards (RLVR)
✓ Understand how LLMs learn to reason and provide chains of thought
✓ Understand how LLMs get trained to call other tools and collaborate with other LLMs/Agents

Requirements
● Basic probability & statistics understanding (e.g. : distributions, mean, variance, expectation)
● Basic linear algebra and calculus
● Good knowledge of neural networks and deep learning (e.g. : gradient descent, back-propagation)

Description
• This is a comprehensive deep dive into reinforcement learning course. It is university-level deep.

• The course starts from the very basics of RL in constrained simple problems and progresses with complexity step by step until the introduction of algorithms capable of solving complex real world problems for discrete actions (e.g.: LLMs) and continuous (e.g.: Robotics).

• The course is also highly mathematical. It introduces a lot of algorithms, proofs, and derivations. However, it is still highly intuitive as well. Lots of intuitive examples to explain every concept or idea are provided.

• While there are some code examples, I don’t view this as the main goal of the course. The course focuses much more on concepts, intuitions, and derivations. Coding is used mainly for illustration.

• The course covers a lot of traditional and SOTA algorithms in rich & satisfying detail. Some algorithms covered in this course are: Iterative Policy Evaluation (PE), Value Iteration (VI), Policy Iteration (PI), Monte-Carlo evaluation, TD(0), TD(lambda), Backward TD(lambda) with eligibility traces, SARSA, Q-Learning, Double Q-Learning, Expected SARSA, Deep SARSA, Deep Q-Learning, Deep Double Q-Learning, REINFORCE, A2C, A3C, DDPG, SAC, TRPO, PPO, GRPO, DPO.

• Finally, the course has a sizeable case study section on: RL with LLMs. It covers how large language models and chatting agents are trained using reinforcement learning to have better alignment with human preferences, produce chains of thought, and to be better at math & coding. Algorithms for RLHF & RLVR are covered in deep detail.

Who this course is for
■ University students taking a serious reinforcement learning course
■ Machine learning engineering looking to get a deeper understanding of reinforcement learning
■ LLM engineers looking to understand the inner workings of RLHF and RLVR

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