Reinforcement Learning for Control: Teaching Robots to Act Through Rewards
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Course Modules:
Module 1: Introduction to RL for Control
What makes control tasks ideal for reinforcement learning?
Differences between classical control and learning-based control
Real-world applications: drones, bipedal robots, smart vehicles
Module 2: Modeling Control Tasks as MDPs
Markov Decision Process (MDP) formulation
Defining states, actions, rewards, and transitions
Designing meaningful reward functions for control tasks
Module 3: Key RL Algorithms for Control
Q-learning and Deep Q-Networks (DQN)
Policy Gradient Methods (REINFORCE, Actor-Critic)
Advanced techniques: PPO (Proximal Policy Optimization), SAC (Soft Actor-Critic)
Module 4: Training Agents for Dynamic Control
Simulating control tasks (e.g., CartPole, MountainCar, Pendulum)
Stabilization, convergence, and policy evaluation
Tuning hyperparameters for motion smoothness and response time
Module 5: Real-World Challenges & Transfer
Sim-to-real transfer in robotics
Safety, exploration limits, and constrained environments
Generalization across multiple control scenarios
Module 6: Capstone Project – Train a Control Agent
Select a control environment (e.g., CartPole, drone stabilization, robotic arm)
Implement and train an RL agent using appropriate algorithm
Submit performance graphs, learned policies, and system behavior analysis
Tools & Technologies Used:
Python
OpenAI Gym (CartPole, Pendulum, MountainCar)
PyTorch / TensorFlow
RL libraries: Stable-Baselines3, Spinning Up, or Ray RLlib
Target Audience:
AI learners focused on robotics and automation
Control systems engineers exploring ML integration
Students in mechanical/electrical engineering and computer science
Developers building adaptive real-time systems
Global Learning Benefits:
Move beyond traditional control logic with AI-powered adaptation
Apply RL to real-time, continuous, and dynamic systems
Understand how AI learns to balance and navigate
Prepare for careers in smart robotics, autonomous vehicles, and AI control design
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