How Exploration Agents like Q-Learning, UCB, and MCTS Collaboratively Learn Intelligent Problem-Solving Strategies in Dynamic Grid Environments

In this tutorial, we explore how exploration strategies shape intelligent decision-making through agent-based problem solving. We build and train three agents, Q-Learning with epsilon-greedy exploration, Upper Confidence Bound (UCB), and Monte Carlo Tree Search (MCTS), to navigate a grid world and reach a goal efficiently while avoiding obstacles. Also, we experiment with different ways of […]

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