Robot Navigation Analysis - Part 1: The Theoretical Foundation скачать в хорошем качестве

Robot Navigation Analysis - Part 1: The Theoretical Foundation

In this video, we break down the fundamental theory behind Reinforcement Learning (RL) and how it can be used to teach a robot to navigate a maze. Before jumping into the code (Part 2), it is critical to understand the mathematical framework that makes learning possible. We explore how an Agent interacts with an Environment to maximize rewards, converting a blind search into an optimal pathfinding strategy using Q-Learning. The RL Loop: Agent, Environment, State, Action, Reward. Markov Decision Processes (MDPs): Defining the world mathematically. The Bellman Equation: The recursive formula that solves for optimal value. Q-Learning: How the agent learns from trial and error. Exploration vs. Exploitation: Balancing curiosity with efficiency (Epsilon-Greedy). This is Part 1 of the Robot Navigation series. In Part 2, we will implement this entire theory in Python, building a custom MazeEnv and a QLearningAgent from scratch. #ReinforcementLearning #Robotics #MachineLearning #Python #QLearning #ArtificialIntelligence #ComputerScience #Theory #Education