EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning

arXiv:2403.10692v1 Announce Type: new
Abstract: Text-based games (TBGs) have emerged as an important collection of NLP tasks, requiring reinforcement learning (RL) agents to combine natural language understanding with reasoning. A key challenge for agents attempting to solve such tasks is to generalize across multiple games and demonstrate good performance on both seen and unseen objects. Purely deep-RL-based approaches may perform well on seen objects; however, they fail to showcase the same performance on unseen objects. Commonsense-infused deep-RL agents may work …

abstract agents arxiv challenge collection cs.ai cs.cl cs.lo exploration games good key language language understanding multiple natural natural language nlp performance reasoning reinforcement reinforcement learning solve tasks text textual type understanding