arXiv:1806.11525
공식 코드 · Papers with Code
Counting to Explore and Generalize in Text-based Games
원문 초록
We propose a recurrent RL agent with an episodic exploration mechanism that helps discovering good policies in text-based game environments. We show promising results on a set of generated text-based games of varying difficulty where the goal is to collect a coin located at the end of a chain of rooms. In contrast to previous text-based RL approaches, we observe that our agent learns policies that generalize to unseen games of greater difficulty.
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SituatedThinker: Grounding LLM Reasoning with Real-World through Situated Thinking
TextArena
Persona Dynamics: Unveiling the Impact of Personality Traits on Agents in Text-Based Games
TextGames: Learning to Self-Play Text-Based Puzzle Games via Language Model Reasoning
STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language Models
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning
토론
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