Reinforcement Learning
Course Overview:
This course equips you with the foundational knowledge of Reinforcement Learning (RL), a powerful AI technique where agents learn through trial and error in dynamic environments. You'll explore how RL can be applied to optimize decision-making and control processes within Supply Chain Management (SCM), leading to improved efficiency and adaptability in your supply chains.
Learning Objectives:
Define Reinforcement Learning and its core principles (rewards, states, actions).
Understand different RL algorithms (Q-learning, Policy Gradients) and their suitability for various SCM applications.
Explore the concept of exploration vs. exploitation and its importance in RL training.
Identify real-world applications of Reinforcement Learning in Supply Chain Management (e.g., dynamic inventory management, autonomous vehicle routing, warehouse robot control).
Analyze the challenges and limitations of implementing RL in real-world SCM scenarios.
Course Highlights:
1. Unveiling Reinforcement Learning
Introduction to Reinforcement Learning: Learning through trial and error in dynamic environments.
Demystifying the core concepts of RL: Agents, environments, states, actions, and rewards.
Understanding the concept of an agent and its interaction with the environment.
Exploring different RL algorithms: Q-learning and its approach to maximizing rewards.
Hands-on Exercises (Optional): Utilizing online simulation environments to experiment with basic RL algorithms.
Case Studies: Exploring early applications of RL in dynamic inventory management for optimizing stock levels.
2. Advanced RL Concepts and Exploration
Introduction to Policy Gradient methods: Training agents to learn optimal policies for decision-making.
Understanding the concept of exploration vs. exploitation and its importance in RL training.
Deep dive into specific Policy Gradient algorithms (e.g., REINFORCE, Actor-Critic methods).
Hands-on Exercises (Optional): Exploring Policy Gradient algorithms in online simulation environments.
Case Studies: Exploring applications of RL for autonomous vehicle routing in complex transportation networks.
3. Reinforcement Learning for SCM Applications and Beyond
Exploring RL applications in warehouse management: Training RL agents to control robots for picking and sorting tasks.
Reinforcement Learning for demand forecasting: Optimizing forecasting models through interaction with real-time data.
Course Wrap-up: Addressing challenges of implementing RL in SCM (data limitations, computational complexity).
Exploring the future of RL in SCM: Combining with other AI techniques for even more powerful solutions.
Prerequisites:
Strong understanding of linear algebra, calculus, and probability theory
Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)
Familiarity with basic machine learning concepts and techniques (e.g., supervised learning, neural networks)
Knowledge of Markov Decision Processes (MDPs) is beneficial but not required