RAL lets an LLM agent learn from its own play by proposing strategies, validating them in one-step state transitions, and retrieving condensed experiences, improving StarCraft II decision-making without any model training.
Large Language Models Play StarCraft II: Benchmarks and a Chain of Summarization Approach
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Retrieval Augmented Learning: A Retrial-based Large Language Model Self-Supervised Learning and Autonomous Knowledge Generation
RAL lets an LLM agent learn from its own play by proposing strategies, validating them in one-step state transitions, and retrieving condensed experiences, improving StarCraft II decision-making without any model training.