Text-based games provide valuable environments for language-based autonomous
agents. Cependant, planning-then-learning paradigms, such as those combining
Monte Carlo Tree Search (MCTS) and reinforcement learning (RL), are notably
time-consuming due to extensive iterations. Additionally, these algorithms
perform uncertainty-driven exploration but lack language understanding and
reasoning abilities. Dans ce document, we introduce the Monte Carlo planning with
Dynamic Memory-guided Large language model (MC-DML) algorithm. MC-DML leverages
the language understanding and reasoning capabilities of Large Language Models
(LLM) alongside the exploratory advantages of tree search algorithms.
Spécifiquement, we enhance LLMs with in-trial and cross-trial memory mechanisms,
enabling them to learn from past experiences and dynamically adjust action
evaluations during planning. We conduct experiments on a series of text-based
games from the Jericho benchmark. Our results demonstrate that the MC-DML
algorithm significantly enhances performance across various games at the
initial planning phase, outperforming strong contemporary methods that require
multiple iterations. This demonstrates the effectiveness of our algorithm,
paving the way for more efficient language-grounded planning in complex
environments.

Cet article explore les excursions dans le temps et leurs implications.

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