Text Based Reinforcement Learning

Text-based reinforcement learning (RL) focuses on training agents to interact with environments described solely through text, achieving goals specified in natural language. Current research emphasizes improving agent generalization and interpretability, exploring methods like self-supervised learning with large language models to generate training data and employing neuro-symbolic architectures that combine neural networks with symbolic reasoning to enhance both performance and explainability. These advancements hold significant potential for creating more robust and adaptable AI agents capable of complex interactions in diverse textual environments, impacting fields such as game AI, interactive storytelling, and human-computer interaction.