How Exploration Agents like Q-Learning, UCB, and MCTS Collaboratively Learn Intelligent Problem-Solving Strategies in Dynamic Grid Environments
In this tutorial, we discover how exploration methods form clever decision-making via agent-based downside fixing. We construct and prepare three brokers, Q-Learning with epsilon-greedy exploration, Upper Confidence Bound (UCB), and Monte Carlo Tree Search (MCTS), to navigate a grid world and attain a aim effectively whereas avoiding obstacles. Also, we experiment with alternative ways of…
