Design and Optimization of Reinforcement Learning-Based Agents in Text-Based Games
TLDR
Proposes deep reinforcement learning agent with world model for text-based games, achieving improved game completion and win rates.
Reasoning
The paper presents a clear combination of deep learning for world modeling and policy gradient for agent learning in text-based games, with empirical results showing improvement. However, the abstract lacks details on the specific architecture, comparison baselines, and generalizability beyond text-based games.
Read-first score
Read-first score 40.3, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 22.
Field roles
Rank sensitivity
Stability: volatile; rank range: 304.
Keyword Scores
Deep Analysis
Innovations
- Novel approach combining deep learning-based world model with policy gradient reinforcement learning for text-based games
- Application of deep learning to process game text and build a world model
- Policy gradient-based deep reinforcement learning method to convert state value to optimal policy
Methodology
A deep learning model is first applied to process game text and build a world model. Then, the agent is learned through a policy gradient-based deep reinforcement learning method to facilitate conversion from state value to optimal policy.
Key Results
The enhanced agent works better in several text-based game experiments and significantly surpasses previous agents on game completion ratio and win rate.