Policy Gradient Methods: Direct Optimization for Reinforcement Learning
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Course Modules:
Module 1: Introduction to Policy-Based Reinforcement Learning
What are policy gradient methods?
Differences between value-based and policy-based RL
When and why to use policy optimization
Module 2: Stochastic Policies and the Policy Objective
Probability-based decision-making in RL
Defining the policy π(a|s; θ)
The goal: maximize expected cumulative reward
Module 3: The REINFORCE Algorithm
Monte Carlo policy gradient estimation
The score function and policy gradient theorem
Implementing REINFORCE in Python
Module 4: Variance Reduction and Advantage Functions
Why policy gradients are high variance
Using baselines and advantage estimators
Generalized Advantage Estimation (GAE) intro
Module 5: Actor-Critic Methods
Combining policy (actor) and value (critic) networks
On-policy vs. off-policy training
Applications in continuous control and robotics
Module 6: Capstone Project – Train a Policy Gradient Agent
Choose a Gym environment with a continuous or stochastic policy need
Implement REINFORCE or actor-critic
Visualize training results, return curves, and policy behavior
Tools & Technologies Used:
Python
PyTorch or TensorFlow (for neural policy networks)
OpenAI Gym
Matplotlib / Seaborn (for plots and training curves)
Target Audience:
Intermediate to advanced RL learners
AI engineers and researchers
Developers exploring robotics or continuous action agents
Students interested in scalable policy optimization
Global Learning Benefits:
Learn to train agents with stochastic and continuous policies
Apply deep reinforcement learning to real-world tasks
Understand how to balance exploration, variance, and learning speed
Gain hands-on experience with advanced RL algorithms
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