Autoregressive Action Selection for Cross-Domain Agent Control
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Solution Overview
Problem
Existing machine learning models struggle to effectively control agents in diverse environments and tasks due to their limited ability to generalize across different dimensionalities of observations and actions, requiring extensive training data and computational resources.
Innovation Solution
An autoregressive action selection neural network that represents observations and actions as sequences of data elements, enabling training on a diverse set of examples and facilitating 'few-shot' or 'zero-shot' learning across various domains by leveraging a flexible and transferable understanding of agent control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional machine learning models are used for agent control, then training data and computational resources are required, but the models struggle to generalize across different dimensionalities of observations and actions
Solution Approach 1:
The patent applies universality by designing a unified neural network architecture that handles multiple types of inputs (observations, actions, states) and multiple tasks (prediction, control, planning) through a single model. The model processes diverse data dimensionalities using embedding layers and attention mechanisms, eliminating the need for separate models for different observation-action dimensionalities and reducing training data requirements across domains.
2Adaptability or versatility
If traditional machine learning models are used for agent control, then extensive training data is required, but computational resources are excessively consumed
Solution Approach 1:
The patent replaces traditional mechanical training approaches (extensive data processing, multiple specialized models) with a neural network-based system that learns representations automatically. The attention mechanisms and embedding layers substitute for manual feature engineering and reduce computational overhead by processing diverse inputs through a unified architecture, lowering energy consumption while maintaining generalization capability.
3Productivity
If autoregressive action selection is used, then efficient and adaptable agent control is achieved, but the system requires processing sequences of data elements
Solution Approach 1:
The patent applies segmentation by breaking down the action selection process into discrete sequential steps: generating current state representation, autoregressively generating action sequences position by position, and executing actions. The system processes observations, states, and actions as segmented sequences of data elements, allowing efficient autoregressive control while managing complexity through structured sequence processing and modular neural network components.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent to interact with an environment using an action selection neural network. In one aspect, a method comprises, at each time step in a sequence of time steps: generating a current representation of a state of a task being performed by the agent in the environment as of the current time step as a sequence of data elements; autoregressively generating a sequence of data elements representing a current action to be performed by the agent at the current time step; and after autoregressively generating the sequence of data elements representing the current action, causing the agent to perform the current action at the current time step.