Comprehensive Exercise

A comprehensive hands-on exercise implementing and training various reinforcement learning algorithms for control problems, from classic game environments to advanced control system applications.

📋 Overview

This exercise is structured in four main parts, progressively building from fundamental RL concepts to advanced control applications:

1. Actor-Critic Algorithm - Learn policy gradient methods with value function approximation
2. DDPG (Deep Deterministic Policy Gradient) - Implement continuous control with actor-critic networks
3. DDPG for Control Problem I - Apply DDPG to a practical control system
4. DDPG for Control Problem II - Extend to a more complex control scenario

🎯 Learning Objectives

• Understand neural network-based policy and value function approximation
• Implement policy gradient algorithms (Actor-Critic)
• Develop continuous control algorithms (DDPG)
• Apply RL techniques to real-world control problems
• Train agents and evaluate their performance

🏗️ Project Structure

RLinControl_CE.ipynb
├── Part 0: Setup & Utilities
├── Part 1: Actor-Critic
├── Part 2: DDPG
├── Part 3: DDPG for Control Problem I
└── Part 4: DDPG for Control Problem II

📊 Evaluation Metrics

1. Total Reward per Episode: Immediate performance indicator
2. Average Reward (50-episode MA): Smoothed performance trend
3. Closed-Loop Response: System output under different inputs
4. Convergence Speed: How quickly the agent learns

📈 Expected Results

• Part 1 (Actor-Critic): Episodic reward should converge to ~500 on CartPole
• Part 2 (DDPG): Average reward should trend toward -200 to -300 on Pendulum
• Parts 3-4: Depends on control problem design and reward structure

🔗 References

• Python Control: https://python-control.readthedocs.io/
• Gymnasium: https://gymnasium.farama.org/

👨‍🏫 Course Information

Institution: IUST Type: Comprehensive Exercise Level: Intermediate to Advanced RL

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Last Updated: 2025 Status: Active Exercise with TODO sections for student completion