Summary of Atari-gpt: Benchmarking Multimodal Large Language Models As Low-level Policies in Atari Games, by Nicholas R. Waytowich et al.
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games
by Nicholas R. Waytowich, Devin White, MD Sunbeam, Vinicius G. Goecks
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advancements in large language models (LLMs) have enabled them to integrate visual, auditory, and textual data, expanding their capabilities beyond traditional text-based tasks. This paper introduces a novel benchmark to test 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. The 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. Results show that these multimodal LLMs are not yet capable of being zero-shot low-level policies, partly due to their limited visual and spatial reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special language models called large language models (LLMs) to play Atari games without needing training or specific instructions. These models can understand and interact with different types of data like text, images, and sounds. The researchers created a test to see how well these LLMs do in playing Atari games compared to other methods like traditional computer programs that learn by trial and error. They found that the language models are not yet good enough to play games without any training, but they have potential for future development. |
Keywords
» Artificial intelligence » Reinforcement learning » Zero shot
