Reinforcement Learning | NetrAInsights

Level

Advanced

Duration

3 Weeks

Hands-On Labs

Format

Self-paced

What You'll Learn

Explore the foundations and frontiers of reinforcement learning. From Q-learning to deep RL to RLHF, this course equips you to train agents that learn optimal behavior from interaction with their environment.

RL Foundations: MDP, states, actions, rewards, policies
Classical RL: Q-learning, SARSA, TD methods
Deep RL: DQN, PPO, A3C, SAC
Policy Optimization: REINFORCE, actor-critic, trust region methods
RLHF: Reward modeling for LLM alignment
Multi-agent RL: Cooperative and competitive environments

Course Modules

🎲 Week 1: RL Fundamentals▼

Markov Decision Processes (MDPs)
Value functions and Bellman equations
Dynamic programming (policy/value iteration)
Q-learning and SARSA
Exploration-exploitation tradeoff
Lab 1: Implement Q-learning on FrozenLake
Lab 2: Value iteration for GridWorld
Lab 3: SARSA vs Q-learning comparison
Lab 4: Epsilon-greedy exploration strategies

🧠 Week 2: Deep Reinforcement Learning▼

Deep Q-Networks (DQN) and extensions
Policy gradient methods (REINFORCE)
Actor-Critic and Advantage functions
PPO (Proximal Policy Optimization)
Gymnasium environments
Lab 5: DQN for CartPole
Lab 6: DQN for Atari game
Lab 7: PPO with Stable Baselines3
Lab 8: A3C for continuous control
Lab 9: Custom Gymnasium environment

🤖 Week 3: Advanced RL & RLHF▼

SAC and TD3 for continuous action spaces
Model-based RL (Dreamer, MuZero)
Multi-agent RL frameworks
Reward modeling and RLHF for LLMs
Sim-to-real transfer for robotics
Lab 10: SAC for robotic control (MuJoCo)
Lab 11: Multi-agent competitive game
Lab 12: Reward model training for RLHF
Lab 13: Fine-tune LLM with PPO (TRL)
Lab 14 (Capstone): Train agent to solve complex environment

Prerequisites

Python (strong)
Deep Learning fundamentals
Linear algebra and probability theory
Familiarity with PyTorch

Who Should Take This?

ML Researchers exploring decision-making AI
Robotics Engineers building autonomous systems
Game AI Developers
LLM Engineers applying RLHF

Tools & Tech Stack

Python, PyTorch, NumPy
Gymnasium, MuJoCo, Pettingzoo
Stable Baselines3, CleanRL
TRL (for RLHF with LLMs)
Weights & Biases for experiment tracking

Ready to Start?

Train agents that learn, adapt, and master their environment — from games to robots to language models.

📧 Enroll Now

🎮 Reinforcement Learning