A Scientific Survey of Papers Citing and Surrounding "A Generalist Agent"
arXiv:2205.06175
The paper "A Generalist Agent" arXiv:2205.06175, published in Transactions on Machine Learning Research in 2022, introduced Gato, a groundbreaking multi-modal, multi-task, multi-embodiment generalist agent. This paper marked a significant shift in the field of AI research, moving away from task-specific models towards more general-purpose agents.
The Vision of Gato: One Model for Many Tasks
Gato's core innovation lies in its ability to perform a wide range of tasks using a single neural network with shared weights. It achieves this by representing all data as a flat sequence of tokens, similar to how language models process text. This allows Gato to process various modalities, including text, images, proprioception (sensory information about the agent's body), and actions.
Key aspects of Gato's design:
Multi-Modal, Multi-Task, Multi-Embodiment: Gato can handle tasks involving text, images, proprioception, and continuous and discrete actions. It can also adapt to different embodiments, such as a real robot arm, a simulated environment, or a virtual agent in a game.
Tokenization: Gato converts all data into a unified sequence of tokens, allowing a single transformer network to process it.
Embedding: Tokens are embedded using a parameterized function, taking into account their modality.
Training: Gato is trained on a massive dataset using a masked autoregressive loss function.
Prompt Conditioning: Task demonstrations or instructions are provided as prompts to guide the model towards specific tasks.
Gato's Capabilities:
The paper demonstrated Gato's capabilities across a wide range of tasks, including:
Simulated Control: Playing Atari games, navigating in simulated 3D environments, solving Sokoban puzzles, and more.
Robotics: Stacking blocks with a real robot arm.
Text Generation: Image captioning and basic dialogue.
Gato's performance on these tasks was often comparable to or even exceeding specialized agents trained solely on a single task. This demonstrated the potential of a generalist approach for AI development.