Reinforcement Learning for Control: Teaching Robots to Act Through Rewards

web-development.

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

?Master Study NLP Fundamentals: The Foundation of Language Understanding in AI

?Shop our library of over one million titles and learn anytime

?‍? Learn with our expert tutors