Design and Optimization of Reinforcement Learning-Based Agents in Text-Based Games
This work addresses the challenge of improving AI performance in text-based games, which is an incremental advancement in the domain of reinforcement learning applications.
The paper tackled the problem of designing agents for text-based games using reinforcement learning, resulting in an enhanced agent that significantly surpasses previous agents in game completion ratio and win rate.
As AI technology advances, research in playing text-based games with agents has becomeprogressively popular. In this paper, a novel approach to agent design and agent learning ispresented with the context of reinforcement learning. A model of deep learning is first applied toprocess game text and build a world model. Next, the agent is learned through a policy gradient-based deep reinforcement learning method to facilitate conversion from state value to optimal policy.The enhanced agent works better in several text-based game experiments and significantlysurpasses previous agents on game completion ratio and win rate. Our study introduces novelunderstanding and empirical ground for using reinforcement learning for text games and sets thestage for developing and optimizing reinforcement learning agents for more general domains andproblems.