Policy Gradient Methods
Implement policy gradient algorithms — REINFORCE, PPO, and Actor-Critic.
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After this skill you can…
- Implement REINFORCE from scratch
- Train a PPO agent with Stable-Baselines3
- Explain the advantage function in Actor-Critic
Prerequisites
Watch (10 videos)
Direct Preference Optimization (DPO): End-to-End Implementation
→ Optimize DPO policies→ Improve DPO stability and efficiency
Lecture 12: Other Social Insurance Programs
→ Evaluate public policy effectiveness→ Design social insurance programs
Stanford CS224R Deep Reinforcement Learning | Spring 2025 | Lecture 6: Q-Learning
→ Learn policy gradient methods→ Understand RL without explicit policy learning
Stanford CS224R Deep Reinforcement Learning | Spring 2025 | Lecture 8: Reward Learning
→ Apply inverse reinforcement learning techniques→ Implement reward shaping methods
Stanford CS224R Deep Reinforcement Learning | Spring 2025 | Lecture 4: Actor-Critic Methods
→ Implement actor-critic methods→ Use policy estimation to improve RL algorithms
Stanford CS224R Deep Reinforcement Learning | Spring 2025 | Lecture 13: Meta RL
→ Design meta-RL algorithms→ Implement black-box meta-RL methods
Stanford CS224R Deep Reinforcement Learning | Spring 2025 | Lecture 15: Hierarchical RL and IL
→ Implement policy gradient methods→ Optimize RL policies
Stanford CS224R Deep Reinforcement Learning | Spring 2025 | Lecture 3: Policy Gradients
→ Apply policy gradient methods→ Analyze policy optimization problems
Q-learning with Flow-Matching Policies
→ Optimize policies for robotic manipulation→ Apply reinforcement learning to real-world problems
Stanford CS224R Deep Reinforcement Learning | Spring 2025 | Lecture 2: Imitation Learning
→ Implement Policy Gradient Methods→ Understand Imitation Learning Basics
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