Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games (2408.15950v2)

Abstract: Recent advancements in LLMs have expanded their capabilities beyond traditional text-based tasks to multimodal domains, integrating visual, auditory, and textual data. While multimodal LLMs have been extensively explored for high-level planning in domains like robotics and games, their potential as low-level controllers remains largely untapped. In this paper, we introduce a novel benchmark aimed at testing the emergent capabilities of multimodal LLMs as low-level policies in Atari games. Unlike traditional reinforcement learning (RL) methods that require training for each new environment and reward function specification, these LLMs utilize pre-existing multimodal knowledge to directly engage with game environments. Our study assesses the performances of multiple multimodal LLMs against traditional RL agents, human players, and random agents, focusing on their ability to understand and interact with complex visual scenes and formulate strategic responses. Our results show that these multimodal LLMs are not yet capable of being zero-shot low-level policies. Furthermore, we see that this is, in part, due to their visual and spatial reasoning. Additional results and videos are available on our project webpage: https://dev1nw.github.io/atari-gpt/.

Insights into Atari-GPT: LLMs as Low-Level Policies in Atari Games

The paper "Atari-GPT: Investigating the Capabilities of Multimodal LLMs as Low-Level Policies for Atari Games" explores an innovative application of multimodal LLMs in the field of video games, specifically within Atari environments. The authors examine the potential of these models as low-level controllers, comparing their performance to traditional Reinforcement Learning (RL) agents, humans, and random agents. This paper opens doors to utilizing the multimodal capabilities of LLMs in dynamic, visually complex tasks, providing a new benchmark—Atari-GPT—for evaluating LLMs in such contexts.

Overview of the Approach

The research is grounded in the exploration of whether state-of-the-art multimodal LLMs, such as GPT-4V Turbo and GPT-4o, can function effectively as low-level policies by directly engaging with the game environment. Unlike traditional RL and imitation learning methods that require extensive computational resources and precise reward function design, LLMs leverage their pre-training to perform immediate gameplay without further tuning. The paper investigates their performance via game scores and analyzes visual understanding, spatial reasoning, and strategic capabilities.

Experimental Framework

The authors structured their research around three pivotal questions concerning the functionality, scene understanding, and strategic output of multimodal LLMs. They employed two main experimental paths: game-play experiments and understanding and reasoning tasks. The LLMs were evaluated in seven Atari environments, testing their ability to formulate effective strategies based on current game states. Additionally, researchers examined the role of in-context learning by providing demonstration examples before gameplay, aiming to see if models could enhance their performance with additional game context.

Results and Comparative Analysis

The results demonstrate that larger LLMs like GPT-4V Turbo and GPT-4o outperform smaller ones like Gemini 1.5 Flash and exhibit a significant understanding of game mechanics. Nonetheless, none match the proficiency of RL agents or humans. Notably, the paper found that introducing in-context learning had little to no impact on the average game-playing performance, indicating a challenge in leveraging additional contextual data to improve strategies meaningfully. This suggests that while LLMs can comprehend the visual scenes, their ability to act efficiently based on that understanding remains limited.

Furthermore, the paper highlights that spatial reasoning poses a significant challenge for these models, often leading the LLMs to underperform in terms of decision-making when compared to human benchmarks. The environment in which LLMs showed the most promise was Space Invaders; however, performance variations across other games indicate the complexity and nuanced requirements of low-level control tasks.

Implications and Future Directions

This work represents an initial foray into utilizing LLMs beyond traditional textual or multimodal tasks, showcasing their potential and current limits in acting as low-level decision-makers in gaming environments. The outcomes suggest the necessity for more targeted fine-tuning and possibly architectural enhancements that can cater to real-time and context-sensitive tasks such as video games.

In terms of theoretical implications, this paper provides insights into the adaptability and generalization capabilities of LLMs across domains they were not explicitly trained for. Practically, as LLMs evolve and become more capable of nuanced reasoning, they may serve broader roles in automated control systems beyond gaming.

Future research might focus on enhancing the pathways through which these models interpret and react to game dynamics, possibly integrating more sophisticated in-context learning capabilities or developing external memory systems to aid in strategic decision-making. As LLM architecture and training methodologies advance, establishing benchmarks like Atari-GPT ensures sustained progress tracking and comparative evaluations across models and methods.

By positioning this work at the intersection of AI research and practical gaming applications, the paper provides a foundation for further exploration into how multimodal LLMs can transition into diverse operational domains, potentially enriching the field of AI through innovative cross-modal applications.