Unlocking the Secrets of Human Behavior | AI Scholar Demo 2021

Table of Contents

1. Introduction
2. Background
3. The Problem with Assimilation of Behaviors in Machine Learning Models
4. The Need for Steerable Models
5. The Proposed Solution: Disentangling Data with a Framework
6. Experimental Setup and Methodology
7. Architecture of the Framework
8. Training Process and Objectives
9. Demonstration of the Framework
10. Limitations and Future Directions

Introduction

In this article, we will explore a framework that aims to disentangle data containing multiple behaviors from different experts in order to steer machine learning models towards specific modes of behavior. We will discuss the challenges associated with assimilation of behaviors in models and the need for steerable models. The proposed solution will be detailed, including the experimental setup and methodology. The architecture of the framework and the training process will be explained. Finally, we will demonstrate the framework's capabilities and discuss its limitations and potential future directions.

Background

The internet is a vast source of data that is widely used for training machine learning models. However, this data is produced by individuals or organizations with their own utility functions, which influence the behaviors contained within the data. When models are trained using this data, they tend to assimilate and reproduce these behaviors. As researchers and designers, there is often a need to steer the trained models towards specific modes of behavior or away from others. This is particularly important as models are applied to increasingly complex and diverse settings.

The Problem with Assimilation of Behaviors in Machine Learning Models

The assimilation of behaviors in machine learning models poses several challenges. Firstly, it can lead to a lack of control over the behavior of the models, as they reproduce the behaviors contained in the training data indiscriminately. This can be problematic when the desired behavior differs from the behaviors present in the data. Secondly, as models become more capable, there is a need to Align their behavior with the Context or human preferences. Without the ability to steer the behavior of the models, their usefulness and applicability may be limited.

The Need for Steerable Models

Steerable models offer the flexibility to control the behavior of machine learning models and align them with desired outcomes. By disentangling the data and training models to steer towards specific modes of behavior, designers and researchers gain greater control over the models' actions. This allows for more tailored and context-specific imitation, making the models more useful and adaptable to different settings.

The Proposed Solution: Disentangling Data with a Framework

The proposed solution involves the development of a framework that disentangles data containing multiple behaviors from different experts. The objective is to train models to steer towards specific modes of behavior by learning from the disentangled data. This framework incorporates an offline reinforcement learning setting and employs a mode conditional policy to achieve the desired behavior. The process involves collecting samples, clustering them using a VQ-VAE model, and using the clustered information to guide the behavior of a Gaussian MLP actor.

Experimental Setup and Methodology

To test the effectiveness of the framework, a continuous control environment was chosen. This environment allows for explicit design of expert behaviors and evaluation of context-specific imitation. The setup consists of an agent navigating a lane using forward and rotational velocities. There is a goal that resets to a random location when reached, hazards to avoid, and other objects for added complexity. Two custom-designed experts were used for experimentation.

Architecture of the Framework

The framework comprises two main sub-models: the VQ-VAE and the Gaussian MLP actor. The VQ-VAE is responsible for distilling the input data into Meaningful representations and clustering them to Create partitions. These partitions, represented by distance vectors, are used as instructions to guide the behavior of the Gaussian MLP actor. The Gaussian MLP actor generates a probability distribution of actions Based on the state and context vector, which is concatenated with the distances from the VQ-VAE.

Training Process and Objectives

The training process involves optimizing two main objectives: the VQ-VAE objective and the conditional policy loss. The VQ-VAE objective focuses on reconstructing the input data effectively and incentivizing the encoder and decoder to communicate through good latent representations. L2 loss terms are employed to ensure the encoder representations are close to the embeddings and encourage the embeddings to stay close to the encoder representations. The conditional policy loss increases the probabilities of true actions under the conditional normal distribution.

Demonstration of the Framework

The effectiveness of the framework was demonstrated through experiments using the continuous control environment. The clustering ability of the VQ-VAE was evaluated by varying the number of allowed partitions and the step size in state transitions. Results showed that increasing the number of partitions and using larger steps for state transitions improved the model's ability to map different expert behaviors to different latent spaces. The framework was able to steer the model towards specific behaviors, such as goal-seeking or forward movement.

Limitations and Future Directions

While the framework shows promise in disentangling and steering behaviors in machine learning models, there are limitations and areas for future exploration. One limitation is the difficulty in disentangling behaviors when the number of modes is small compared to the number of potential behaviors. Future research could focus on modeling longer-term path dependencies and incorporating continuous information alongside discrete modes. Generalizability and performance guarantees are other areas that warrant further investigation.

Highlights

• The assimilation of behaviors in machine learning models can limit control over their behavior and hinder alignment with desired outcomes.
• Steerable models offer the flexibility to control the behavior of machine learning models and align them with specific modes of behavior.
• The proposed framework disentangles data containing multiple behaviors and enables training models to steer towards desired behaviors.
• The framework incorporates a VQ-VAE for clustering and a Gaussian MLP actor for generating behavior based on clustered information.
• Experiments in a continuous control environment demonstrated the framework's ability to steer models towards specific behaviors.
• Future research can focus on modeling longer-term dependencies and exploring ways to extract generalizable properties from disentangled behaviors.

FAQs

Q: What is the main objective of the proposed framework? A: The main objective of the proposed framework is to disentangle data containing multiple behaviors and train machine learning models to steer towards specific modes of behavior.

Q: How does the framework steer the behavior of models? A: The framework uses a combination of clustering and a conditional policy to guide the behavior of models. Clustering is performed using a VQ-VAE, and the clustered information is used as instructions to guide a Gaussian MLP actor in generating behavior.

Q: Can the framework handle complex and diverse settings? A: Yes, the framework is designed to handle complex and diverse settings. It allows for the alignment of models' behavior with the context or human preferences, making them adaptable to different scenarios.

Q: What are the limitations of the framework? A: One limitation is the challenge of disentangling behaviors when the number of modes is small compared to the number of potential behaviors. Further research is needed to address this limitation and improve behavior disentanglement.

Q: What are some future directions for this research? A: Some future directions include exploring longer-term path dependencies, investigating generalizability and performance guarantees, and experimenting with different modalities and interpretability of the framework.