SPRING: Studying the Paper and Reasoning to Play Games (2305.15486v3)

Abstract: 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 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. Finally, we show the potential of games as a test bed for LLMs.

• The paper introduces SPRING, which uses paper-based context extraction and a QA-DAG reasoning framework to enhance LLM-driven gameplay.
• SPRING employs a LaTeX parser to extract game mechanics and structures strategic decision-making through a directed acyclic graph.
• Experimental results show SPRING integrated with GPT-4 outperforms traditional RL models, achieving higher scores and rewards in zero-shot scenarios.

Overview of the SPRING Framework for Open-World Survival Games

The paper "SPRING: Studying the Paper and Reasoning to Play Games" introduces the SPRING framework, designed to address challenges inherent in open-world survival games using LLMs. Open-world games such as Crafter and Minecraft present substantial hurdles for artificial intelligence, requiring multi-tasking, deep exploration, and goal prioritization. Reinforcement Learning (RL), despite its successes in structured environments, often struggles with these open-world complexities due to high sample complexity and the difficulty in leveraging pre-existing knowledge.

Key Contributions

The SPRING framework proposes a novel approach to game-playing by extracting knowledge from the original academic paper about the game and employing this knowledge in decision-making through LLMs. The system involves two main components:

1. Studying the Paper (Context Extraction): The SPRING framework leverages a knowledge extraction module that reads and parses the \LaTeX source of the original Crafter paper. This module identifies game mechanics and strategies, conditioning the LLM's understanding and providing a deep context for gameplay decision-making.
2. Reasoning Module (QA-DAG Framework): This component organizes the decision-making process through a directed acyclic graph (DAG) that structures a series of questions (QA framework) to guide the LLM through contextual decision-making. Each node in the DAG represents a gameplay-related question, with dependencies representing the logical flow of reasoning. At every game step, the LLM traverses the DAG, and the node responses guide the agent's actions.

Experimental Results

SPRING, when integrated with GPT-4, outperformed state-of-the-art RL models in gameplay tasks for Crafter, achieving superior scores and rewards without the need for conventional RL training. The system was capable of executing sophisticated game trajectories by leveraging contextual prompts, indicating that LLMs with consistent chain-of-thought prompting can efficiently solve complex tasks.

Performance Metrics from the experiments indicated:

• SPRING significantly surpassed DreamerV3, the prior leading RL approach, in both game score and raw reward metrics.
• The zero-shot performance of SPRING demonstrated a competitive edge over RL techniques that usually necessitate millions of training steps.

Analysis of Components

The paper details an extensive component analysis, revealing the critical role of paper-based context extraction in enhancing the LLM's decision-making. It also highlights the necessity of structured reasoning prompts (QA-DAG) in maintaining consistent logical output across varying game scenarios. Importantly, SPRING's competitive performance with GPT-4 underscores the efficiency and applicability of LLMs in dynamic, open-world gaming contexts—full realization hinging on strategic context extraction and well-directed reasoning pathways.

Implications and Future Directions

Practical Implications: The success of SPRING suggests a pathway for integrating LLMs with real-world human-written resources to enhance AI decision-making, circumventing the traditional RL need for vast training datasets. This methodology could extend beyond gaming, applying to simulation-based training or interactive education where contextual understanding from texts is pivotal.

Theoretical Insights: This research underscores the potential for LLMs to act as high-level planners within complex environments. It opens discussions on the future interplay between LLMs and RL, advocating for systems that can exploit written knowledge bases to inform strategic planning.

Future Research Directions: The paper suggests further exploration into integrating SPRING-like frameworks within hierarchical RL settings, using LLMs to generate intrinsic motivations and refine sub-task hierarchies. Additionally, improvements in visual-language understanding would deepen the interaction capabilities of such systems within real-world and simulated settings.

Overall, the SPRING framework offers an innovative lens on leveraging linguistic knowledge bases in AI-driven gameplay, positioning LLMs as pivotal tools for enhancing AI engagement with complex, unstructured environments. The results prompt broader investigations into the capacity of LLMs to generalize across domains by efficiently consuming and utilizing structured textual information.