Scaling Laws for Imitation Learning in Single-Agent Games: Investigating the Impact of Scaling Up Model and Data Size

Core Concepts

Carefully scaling up model and data size can lead to significant improvements in imitation learning performance for single-agent games, as demonstrated in Atari games and NetHack.

Abstract

In this study, the authors explore the impact of scaling up model and data size on imitation learning performance in single-agent games. They find that loss and mean return follow power law trends with respect to FLOPs, showing predictable improvements. The findings suggest a promising path towards training increasingly capable agents for challenging games like NetHack.
The study focuses on demonstrating how scaling laws apply to imitation learning agents trained with behavioral cloning (BC) in both Atari games and NetHack. By analyzing isoFLOP profiles, the authors show clear correlations between model size, number of samples, loss, and mean return with compute budget (FLOPs). The results indicate that improvements in loss translate into better performing agents in the environment.
The research extends its analysis to reinforcement learning (RL) briefly, finding similar power law trends for model size and number of interactions in NetHack. By forecasting compute-optimal BC agents for NetHack, the study shows significant performance improvements compared to prior state-of-the-art approaches. Overall, the findings highlight the potential benefits of scaling up model and data size for training more capable game agents.

Stats

We find clear parabolas with well-defined minima at the optimal model size for a given compute budget.
Loss-optimal data points are used to fit regressions regressing log parameters on log FLOPs.
Power laws are derived for loss-optimal model size, number of training samples, and minimal validation loss.
The average return follows a power law trend with respect to optimal cross-entropy loss.

Quotes

"Scaling up could have unknown unintended consequences."
"While we do not see a direct path towards any negative applications."
"The results suggest a promising path towards increasingly capable game agents."

Key Insights Distilled From

Scaling Laws for Imitation Learning in Single-Agent Games

by Jens Tuyls,D... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2307.09423.pdf

Deeper Inquiries

How might scaling laws impact other domains beyond single-agent games?

Scaling laws can have a significant impact on various domains beyond single-agent games. By understanding how model and data size affect performance in imitation learning, researchers can apply similar principles to fields like natural language processing, computer vision, robotics, and healthcare. Scaling up models could lead to more accurate predictions, improved decision-making processes, and enhanced problem-solving capabilities across different industries. For example, in healthcare, scaling laws could help optimize treatment plans based on patient data and medical records. In finance, it could improve risk assessment models for investment strategies.

What potential unintended consequences could arise from scaling up models?

While scaling up models can bring about numerous benefits, there are also potential unintended consequences to consider. One major concern is the increased computational resources required for training larger models which may contribute to higher energy consumption and environmental impact. Additionally, as models become more complex with scale, they may become harder to interpret or debug leading to issues of transparency and accountability in decision-making processes. There is also a risk of overfitting when scaling up too quickly without proper validation procedures in place.

How can human trajectories be leveraged to improve imitation learning performance?

Human trajectories offer valuable insights into expert behavior that can significantly enhance imitation learning performance. By leveraging human demonstrations or expert playthroughs as training data instead of solely relying on AI-generated trajectories, algorithms can learn more nuanced strategies and behaviors that align with human expertise. Techniques such as Behavioral Cloning from Observation (BCO) allow agents to imitate human actions directly from observational data without explicit reward signals. This approach not only improves generalization but also ensures that the learned policies are aligned with human preferences and ethical standards.