Q-Learning, UCB and MCTS explore how agents can collaboratively learn intelligent problem-solving strategies in dynamic grid environments
In this tutorial, we explore how exploration strategies can shape intelligent decision-making through agent-based problem solving. We built and trained three agents, namely Q-Learning with epsilon-greedy exploration, Upper Confidence Bound (UCB), and Monte Carlo...