Deep-RL

My knowledge source and research material for delving into the field of RL and Deep Reinforcement Learning

Lecture Videos

For understanding Deep Reinforcement Learning, you need to first understand the basics of RL.

Introduction to Deep Reinforcement Learning by Dr. Aditya Nigam

1. Lecture 1: Mathematical Formulation of RL using MDP
2. Lecture 2: Optimal Policy (pi*), Optimal Value and Q-Fn
3. Lecture 3: Policy Evaluation, Value Iteration, Policy Iteration, Policy Improvement (with example)
4. Lecture 4: Deep Q-Learning with Experience Replay

Foundations of Deep RL by Dr. Pieter Abbeel

1. L1 MDPs, Exact Solution Methods, Max-ent RL
2. L2 Deep Q-Learning
3. L3 Policy Gradients and Advantage Estimation
4. L4 TRPO and PPO
5. L5 DDPG and SAC
6. L6 Model-based RL

However, books are considered to be boring, but this book will give you a very well explained in-depth review of Deep Reinforcement Learning namely Deep Reinforcement Learning in Python

@article{SubOptimalDeepRL2021,
    author = {Goel, Anoushkrit},
    title = {{Sub-Optimal way to Deep Reinforcement Learning}},
    year = {2021}
}