Code Index

This directory contains the companion code for each chapter of the course. It is recommended to first enter the code/ directory, then install dependencies and run scripts on a per-chapter basis.

Quick Start

python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# Full set of dependencies, suitable when working through the whole course
pip install -r requirements.txt

# Or install dependencies for just one chapter
pip install -r chapter01_cartpole/requirements.txt

Chapter 1 scripts accept --device auto|cuda|mps|cpu (auto prefers CUDA, then Apple MPS, then CPU). Shared helpers live in device_utils.py.

Chapter Code Overview

Chapter	Directory	Main Code	Description
Ch01 CartPole	chapter01_cartpole/	1-ppo_cartpole.py	SB3 PPO trains CartPole, logging SwanLab metrics
		2-pytorch_ppo.py	Pure PyTorch PPO: Actor-Critic, GAE, clipping, metric logging
		plot_curves.py	Plots reviewable metric curves from the CSV exported by training
		capture_frames.py	Loads a model and captures real Gymnasium rendered frames
Ch02 DPO	chapter17_dpo/	0-download_model.py	Downloads Qwen2.5-0.5B-Instruct
		1-generate_data.py	Generates preference data correcting excessive compliance
		2-test_before.py	Test before fine-tuning
		3-train_dpo.py	TRL DPO training
		4-test_after.py	Test after fine-tuning
Ch03 MDP	chapter03_mdp/	two_armed_bandit.py	Two-armed bandit policy comparison
		bellman_equation_verify.py	Numerical verification of the Bellman equation
		gridworld_q_learning.py	GridWorld Q-Learning and path visualization
Ch04 DQN	chapter04_dqn/	dqn_cartpole.py	DQN implemented from scratch to train CartPole
		double_dqn_cartpole.py	DQN vs. Double DQN comparison
		dqn_gym_sb3.py	SB3 DQN trains CartPole, MountainCar, LunarLander, and other discrete environments, logging SwanLab and evaluation curves
		dqn_atari_sb3.py	SB3 DQN trains Atari for real, including wrapper, SwanLab, evaluation, and logging
		export_dqn_curves.py	Exports lecture figures from the Chapter 4 DQN eval CSV
		dqn_pokemon_red_pyboy.py	PyBoy + SB3 DQN trains an early Pokemon exploration task
Ch05 Policy Gradient	chapter05_policy_gradient/	reinforce_cartpole.py	REINFORCE trains CartPole
		reinforce_with_baseline.py	REINFORCE vs. baseline comparison
		actor_critic_cartpole.py	Actor-Critic and TD error
Ch07 PPO	chapter07_ppo/	ppo_lunar_lander.py	SB3 PPO trains LunarLander-v3
		ppo_from_scratch.py	Pure PyTorch PPO
		gae_visualization.py	GAE parameter visualization
Ch08 RLHF	chapter08_rlhf/	sft_pipeline.py	SFT pipeline
		reward_model_training.py	Reward model training
		rlhf_ppo_train.py	Simplified PPO-RLHF training loop
	chapter08_rlhf/verl_gsm8k/	run_qwen2_5_0_5b_ppo_single_gpu.sh	8.7 veRL + GSM8K external framework adaptation script
Ch09 Alignment	chapter09_alignment/	dpo_hands_on.py	DPO alignment and beta comparison
		dpo_math_reward.py	DPO experiments on math preference data
Ch09 GRPO/RLVR	chapter09_grpo_rlvr/	grpo_mechanism.py	GRPO mechanism demonstration
		grpo_math_reasoning.py	Small GRPO experiment on math reasoning
		rule_based_reward.py	Rule-based reward function
Ch09 Continuous Control	chapter09_continuous_control/	sac_halfcheetah.py	SAC trains HalfCheetah-v4
		ppo_td3_sac_comparison.py	PPO, TD3, SAC comparison
Ch10 Agentic RL	chapter10_agentic_rl/	tool_use_agent.py	Tool selection policy training
		multi_turn_rl.py	Multi-turn interaction credit assignment
		generate_synthetic_data.py	Synthetic trajectory data
		mini_deep_research_grpo.py	Mini Deep Research GRPO example
Ch11 VLM RL	chapter11_vlm_rl/	geometry_counting_dataset.py	Geometry counting dataset
		multi_modal_reward.py	Multi-modal rule-based reward
		vlm_grpo_train.py	VLM GRPO training example
Ch12 Future Trends	chapter12_future_trends/	tree_of_thought.py	Tree of Thought search demonstration
		multi_agent_marl.py	Multi-agent GridWorld
Appendix Pitfalls	appendix_common_pitfalls/	debug_reward_hacking.py	Reward hacking reproduction
		debug_training_collapse.py	Training collapse diagnosis

Notes

• The requirements.txt in each chapter directory contains the minimal dependencies for that chapter.
• LLM-related chapters use small models by default, but running in a GPU environment is still recommended.
• Some scripts will generate output/, model weights, or image files in the current working directory.
• The Chapter 4 DQN scripts use SwanLab local mode by default; after running, you can view the curves with swanlab watch swanlog.