What will you learn? In this comprehensive guide, you will delve into the world of Reinforcement Learning and discover how to guide an agent towards converging at the lowest possible reward. By making strategic adjustments to the reinforcement learning algorithm’s parameters and rewards structure, you will learn how to shape an agent’s behavior to prioritize … Read more
What will you learn? Discover how reinforcement learning techniques can be utilized to solve optimization problems effectively in Python. Introduction to the Problem and Solution Dive into the realm of leveraging reinforcement learning algorithms for solving optimization problems. By amalgamating machine learning with optimization techniques, we can train models to make decisions resulting in optimal … Read more
What will you learn? In this comprehensive guide, you will master the techniques to adjust tensor dimensions effectively while training a Dueling Deep Q-Network (DQN). By understanding how to manipulate tensor shapes efficiently, you’ll enhance your skills in deep reinforcement learning. Introduction to the Problem and Solution Deep reinforcement learning algorithms like DQN rely heavily … Read more
What will you learn? Explore the importance of the “action_history” variable in Tabular Q-Learning for backpropagating q-values efficiently. Introduction to the Problem and Solution In the realm of Tabular Q-Learning, updating q-values based on rewards from actions taken requires a keen consideration of past actions’ influence. The presence of an “action_history” variable proves crucial in … Read more
What will you learn? In this tutorial, you will master the art of integrating a deep reinforcement learning model built using Python and PyTorch into the powerful simulation platform, AnyLogic. By merging these technologies, you’ll be able to create intelligent simulations that adapt dynamically to changing conditions. Introduction to the Problem and Solution Imagine combining … Read more