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Actor-Critic & Advantage Methods: Stabilizing Policy Optimization in Reinforcement Learning
artificial-intelligence-ai.
Course Modules:
Module 1: What Are Actor-Critic Methods?
Role of the actor (policy network) and critic (value estimator)
Differences from pure policy gradient and value-based methods
Why Actor-Critic improves learning efficiency
Module 2: Architecture and Training Pipeline
Shared and separate network structures
Loss functions for actor and critic
Updating both networks via gradients
Module 3: Advantage Estimation
The advantage function A(s, a) = Q(s, a) - V(s)
Why it reduces variance in policy gradients
Introduction to Generalized Advantage Estimation (GAE)
Module 4: Advantage Actor-Critic (A2C)
On-policy learning with synchronous updates
How A2C improves REINFORCE stability
Implementing A2C using Gym and PyTorch or TensorFlow
Module 5: Asynchronous Actor-Critic (A3C)
Parallel learning agents and environments
Benefits in speed and decorrelated exploration
Challenges and multiprocessing considerations
Module 6: Capstone Project – Build an Actor-Critic Agent
Choose an environment (e.g., CartPole, LunarLanderContinuous)
Implement A2C or A3C with advantage estimation
Submit a training report with visualizations and learned policies
Tools & Technologies Used:
Python
OpenAI Gym
PyTorch or TensorFlow (for deep RL models)
Matplotlib, Seaborn (for policy analysis)
Target Audience:
Intermediate to advanced reinforcement learning learners
ML engineers and AI developers working on control problems
Students and researchers exploring deep RL and robotics
Professionals implementing real-time learning systems
Global Learning Benefits:
Learn how to train agents efficiently in complex environments
Combine the strengths of policy gradient and value-based methods
Reduce instability and variance in reinforcement learning
Gain skills in building scalable, parallelized training pipelines
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