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 the challenge of varying dimensionalities of observations and actions, requiring extensive training data and computational resources.
Innovation Solution
An autoregressive action selection neural network that operates on sequences of data elements, enabling flexible and transferable agent control by training on diverse examples, facilitating few-shot and zero-shot learning across different domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing machine learning models are used to control agents in diverse environments, then the models can handle various tasks, but they require extensive training data and computational resources due to varying dimensionalities of observations and actions
Solution Approach 1:
The patent applies universality by designing a single autoregressive transformer model that can handle multiple agent control tasks across diverse environments. The model uses a unified architecture that processes observations and generates actions for different task types (e.g., navigation, manipulation, interaction) without requiring separate specialized models for each environment, thereby reducing the overall computational resources and training data needed while maintaining high adaptability
Solution Approach 2:
The patent employs parameter changes by using autoregressive generation to dynamically produce action sequences of varying lengths and dimensionalities. The model transforms the control problem into a sequence generation task where actions are generated token-by-token, allowing the same model parameters to adapt to different action spaces and observation dimensionalities through the autoregressive process rather than requiring separate parameter sets for each task
2Reliability
If existing machine learning models are trained on extensive data to handle diverse tasks, then the models achieve adequate performance, but the training process becomes computationally expensive and time-consuming
Solution Approach 1:
The patent applies preliminary action by pre-training the transformer model on large-scale language modeling and sequence prediction tasks before fine-tuning on specific agent control tasks. This pre-training establishes robust foundational capabilities that transfer across diverse environments, reducing the amount of task-specific training data and computational resources needed while maintaining high performance reliability
Solution Approach 2:
The patent merges multiple training objectives into a unified framework by combining language modeling, sequence prediction, and agent control tasks during training. This multi-task learning approach allows the model to learn generalizable patterns that improve performance across diverse tasks while reducing the total training time and computational resources compared to training separate models for each task
3Manufacturing precision
If existing machine learning models are designed for specific tasks, then they achieve high performance on those tasks, but they cannot generalize effectively to new domains without extensive retraining
Solution Approach 1:
The patent applies universality by designing a single autoregressive transformer model that can handle multiple agent control tasks across diverse environments. The model uses a unified architecture that processes observations and generates actions for different task types (e.g., navigation, manipulation, interaction) without requiring separate specialized models for each environment, thereby reducing the overall computational resources and training data needed while maintaining high adaptability
Solution Approach 2:
The patent employs parameter changes by using autoregressive generation to dynamically produce action sequences of varying lengths and dimensionalities. The model transforms the control problem into a sequence generation task where actions are generated token-by-token, allowing the same model parameters to adapt to different action spaces and observation dimensionalities through the autoregressive process rather than requiring separate parameter sets for each task
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.