Reinforcement Learning

Reinforcement Learning

RL from first principles to deep policy gradients. Gymnasium environments, Q-learning, DQN, PPO, SAC, multi-agent systems, and applying RL to real-world problems.

FundamentalsTopics 1–10
  • ·What is RL
  • ·Agent and Environment
  • ·Markov Decision Processes
  • ·States and Actions
  • ·Gymnasium Setup
  • ·Exploration vs Exploitation
  • ·Q-Learning
  • ·SARSA
  • ·Monte Carlo Methods
  • ·First RL Program
Start Fundamentals →
IntermediateTopics 1–10
  • ·Deep Q-Networks
  • ·Experience Replay
  • ·Target Networks
  • ·Policy Gradient
  • ·Actor-Critic Methods
  • ·Advantage Functions
  • ·PPO Overview
  • ·Reward Shaping
  • ·Observation Preprocessing
  • ·Training Stability
Start Intermediate →
AdvancedTopics 1–10
  • ·PPO in Depth
  • ·Soft Actor-Critic
  • ·Multi-Agent RL
  • ·Hierarchical RL
  • ·Model-Based RL
  • ·RLHF
  • ·Offline RL
  • ·Curriculum Learning
  • ·Sim-to-Real Transfer
  • ·Evaluating RL Agents
Start Advanced →
AppliedTopics 1–10
  • ·RL in Production
  • ·Environment Design
  • ·Reward Engineering
  • ·Scaling RL Training
  • ·RL for Recommendations
  • ·RL for Robotics
  • ·RL Safety & Alignment
  • ·Evaluation & Benchmarking
  • ·Debugging RL Systems
  • ·RL Infrastructure
Start Applied →