| dc.description.abstract | This thesis investigates the use of Reinforcement Learning (RL) algorithms, specifically QLearning and SARSA, as potential pathfinding solutions in a Unity 2D game environment.
This research explores the capabilities of RL as an alternative approach for autonomous
agents within a gaming framework.
The implementation involves creating a training scene where a single monster agent learns to
navigate the game environment. The agent receives rewards for actions that bring it closer to
the player and penalties for actions leading to less desirable outcomes. Through training, both
the Q-Learning and SARSA models adjust their policies based on these rewards and penalties,
leading to improved pathfinding behavior over time.
The thesis provides a comparative analysis of Q-Learning and SARSA, examining their
effectiveness and efficiency in pathfinding tasks. The findings indicate that reinforcement
learning holds significant potential as an adaptive alternative in complex scenarios. However,
further research and development are necessary to fully realize this potential. Future work
could explore more complex environments, multiple agents, and advanced RL techniques to
enhance the capabilities and applications of this approach. This study provides valuable
insights into using RL for game AI, highlighting both opportunities and current limitations. | en_US |
