Reinforcement Learning: From Basics to Advanced

Comprehensive Reinforcement Learning course — from MDP, Q-Learning to Deep RL platforms with DQN, Policy Gradient, PPO, SAC. Practice game AI, robotics simulation, RLHF for LLM, and deploy RL agents production-ready with Python, Gymnasium, Stable-Baselines3.

Introducing the Series

Reinforcement Learning: From Basics to Advanced is a course that helps you master the entire field of RL — from MDP, Q-Learning platforms to modern Deep RL with PPO, SAC, and RLHF for LLM Alignment.

🎯 After completing the course, you will:

Deep understanding of RL theory: MDP, Bellman, Policy Gradient

Implement DQN, PPO, SAC from scratch

Proficient in using Gymnasium & Stable-Baselines3

Understand RLHF/DPO for LLM alignment

Build RL agents for games, robotics, and real-world problems

Study path

Part 1: RL Platform

MDP, Dynamic Programming, Q-Learning — your first step into the world of RL.

Part 2: Deep Reinforcement Learning

DQN, Policy Gradient, PPO, SAC — when neural networks encounter RL.

Part 3: Frameworks & Practices

Gymnasium, Stable-Baselines3, robotics simulation.

Part 4: RLHF & Production

RL for LLM alignment, multi-agent, and production deployment.

Part 1: RL Foundation — Markov Decision Process & Tabular Methods

Lesson 1: What is Reinforcement Learning? — Agent, Environment & Rewards90 min Lesson 2: Dynamic Programming — Policy Iteration & Value Iteration120 min Lesson 3: Monte Carlo & Temporal Difference Learning150 min Lesson 4: Q-Learning Deep Dive & Multi-Armed Bandits120 min

Part 2: Deep Reinforcement Learning — Neural Networks meet RL

Lesson 5: DQN — Deep Q-Network & Experience Replay180 min Lesson 6: Policy Gradient — REINFORCE & Actor-Critic150 min Lesson 7: PPO — Proximal Policy Optimization180 min Lesson 8: SAC & Advanced Algorithms — Off-Policy Deep RL150 min

Part 3: RL Frameworks & Practice

Lesson 9: Gymnasium & Stable-Baselines3 — Real-life RL Frameworks150 min Lesson 10: Custom Environment Design — Building Game AI150 min Lesson 11: Robotics Simulation — MuJoCo & Isaac Gym180 min

Part 4: RLHF, LLM Alignment & Production

Lesson 12: RLHF — Reinforcement Learning from Human Feedback180 min Lesson 13: DPO & GRPO — Direct Preference Optimization150 min Lesson 14: Multi-Agent RL — Cooperation & Competition150 min Lesson 15: RL Production — Deploy & Monitor RL Agents120 min Lesson 16: Capstone — Building RL Agent for Real-world Problem240 min