An Analysis of Deep Reinforcement Learning Agents for Text-based Games

• URL: http://arxiv.org/abs/2209.04105v2
• Date: Mon, 12 Sep 2022 12:40:19 GMT
• Title: An Analysis of Deep Reinforcement Learning Agents for Text-based Games
• Authors: Chen Chen, Yue Dai, Josiah Poon, Caren Han
• Abstract summary: Text-based games (TBG) are complex environments which allow users or computer agents to make textual interactions and achieve game goals. Finding TBG agent deep learning modules' performance in standardized environments, and testing their performance among different evaluation types is also important for TBG agent research. We constructed a standardized TBG agent with no hand-crafted rules, formally categorized TBG evaluation types, and analyzed selected methods in our environment.
• Score: 4.9702715037812055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-based games(TBG) are complex environments which allow users or computer agents to make textual interactions and achieve game goals.In TBG agent design and training process, balancing the efficiency and performance of the agent models is a major challenge. Finding TBG agent deep learning modules' performance in standardized environments, and testing their performance among different evaluation types is also important for TBG agent research. We constructed a standardized TBG agent with no hand-crafted rules, formally categorized TBG evaluation types, and analyzed selected methods in our environment.

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