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SPRING: Studying Papers and Reasoning to play Games
Yue Wu · Shrimai Prabhumoye · So Yeon Min · Yonatan Bisk · Ruslan Salakhutdinov · Amos Azaria · Tom Mitchell · Yuanzhi Li

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in Knowledge and Logical Reasoning in the Era of Data-driven Learning »

Open-world survival games pose significant challenges for AI algorithms due to their multi-tasking, deep exploration, and goal prioritization requirements. Despite reinforcement learning (RL) being popular for solving games, its high sample complexity limits its effectiveness in complex open-world games like Crafter or Minecraft. We propose a novel approach, SPRING, to read the game's original academic paper and use the knowledge learned to reason and play the game through a large language model (LLM). Prompted with the LaTeX source as game context and a description of the agent's current observation, our SPRING framework employs a directed acyclic graph (DAG) with game-related questions as nodes and dependencies as edges. We identify the optimal action to take in the environment by traversing the DAG and calculating LLM responses for each node in topological order, with the LLM's answer to final node directly translating to environment actions. In our experiments, we study the quality of in-context "reasoning" induced by different forms of prompts under the setting of the Crafter open-world environment. Our experiments suggest that LLMs, when prompted with consistent chain-of-thought, have great potential in completing sophisticated high-level trajectories. Quantitatively, SPRING with GPT-4 outperforms all state-of-the-art RL baselines, trained for 1M steps, without any training.

Author Information

Yue Wu (Carnegie Mellon University)
Shrimai Prabhumoye (NVIDIA)
So Yeon Min (Carngie Mellon University)
Yonatan Bisk (Carnegie Mellon University)
Ruslan Salakhutdinov (Carnegie Mellen University)
Amos Azaria (Ariel University)
Tom Mitchell (CMU)
Yuanzhi Li (CMU)

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