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Phase 25: Reinforcement Learning — Start Here¶

Train agents to make decisions through trial and error — the same technology behind AlphaGo, game-playing AIs, and RLHF for LLMs.

What Is Reinforcement Learning?¶

Agent observes State → takes Action → receives Reward → updates Policy
                              ↑__________________________________↓
                                        (environment loop)

RL is also the engine behind RLHF (Reinforcement Learning from Human Feedback) — how ChatGPT and Claude were trained to be helpful and safe.

Notebooks in This Phase¶

Notebook	Topic
`01_markov_decision_processes.ipynb`	MDPs: states, actions, rewards, Bellman equations
`02_q_learning.ipynb`	Tabular Q-learning and temporal difference learning
`03_deep_q_networks.ipynb`	DQN with neural networks (Atari games)
`04_policy_based_methods.ipynb`	REINFORCE, Actor-Critic, PPO
`05_advanced_topics_applications.ipynb`	RLHF, multi-agent RL, real-world applications
`06_practical_exercises.ipynb`	OpenAI Gym environments, hands-on projects

Key Algorithms¶

Algorithm	Type	Use Case
Q-Learning	Value-based	Simple discrete action spaces
DQN	Value-based	Atari, discrete actions
PPO	Policy-based	Most practical RL tasks
SAC	Actor-Critic	Continuous control (robotics)
RLHF	Human feedback	Fine-tuning LLMs

Prerequisites¶

Neural Networks (Phase 06)
Probability and statistics (Phase 03)
PyTorch basics

Learning Path¶

01_markov_decision_processes.ipynb   ← Start here
02_q_learning.ipynb
03_deep_q_networks.ipynb
04_policy_based_methods.ipynb
05_advanced_topics_applications.ipynb
06_practical_exercises.ipynb         ← Build and train agents