Planning Neural Network Implicit Plan Generation
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Solution Overview
Problem
Current machine learning models lack efficient mechanisms for an agent to generate implicit plans that enable goal-directed behavior and exploration in dynamic environments, limiting their ability to generalize and perform tasks effectively in new situations.
Innovation Solution
The system employs a planning neural network that processes planning embeddings using self-attention mechanisms and residual neural network blocks to generate implicit plans, allowing the agent to iteratively update and share information among past interactions, enabling more effective planning and action selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional machine learning models are used for action selection, then the system is simpler to implement, but the agent cannot generate implicit plans for goal-directed behavior
Solution Approach 1:
The model is segmented into distinct functional components: an embedding generator that creates planning embeddings from past interactions, a planning neural network with self-attention layers that processes these embeddings to generate implicit plans, and an action selection network that converts plans into actions. This segmentation allows each component to specialize in a specific aspect of planning while maintaining overall system manageability
Solution Approach 2:
The patent introduces a new dimensional representation by creating planning embeddings that capture temporal patterns from past interactions across multiple time steps. This embedding dimension transforms raw interaction data into a compressed representation that the planning network can process efficiently, enabling goal-directed behavior without proportionally increasing complexity
2Productivity
If the agent explores the environment extensively to learn tasks, then it gains better understanding of the environment, but it takes more time steps to accomplish tasks
Solution Approach 1:
The planning embeddings are constructed from past interactions and stored in an external memory, preparing the agent with pre-processed environmental understanding before actual task execution. This preliminary organization of knowledge allows the agent to quickly retrieve relevant information during action selection without needing to re-explore the environment
Solution Approach 2:
The planning embeddings serve as an intermediary representation between raw environmental interactions and action selection. These embeddings capture essential environmental patterns and constraints, allowing the agent to make informed decisions without directly experiencing every environmental detail, thus reducing exploration time while maintaining understanding
3Reliability
If the agent uses detailed representations of current observations in planning embeddings, then it has more information for decision making, but it increases computational complexity
Solution Approach 1:
The patent extracts only the most relevant features from current observations by incorporating them selectively into the planning embeddings through the embedding generator. Rather than processing complete observation representations, the system extracts key environmental cues and integrates them with historical interaction patterns, reducing computational load while maintaining decision accuracy
Solution Approach 2:
The planning embeddings dynamically adjust their representation parameters based on the task at hand and the current observation. The embedding generator modifies which features are emphasized in the planning embeddings depending on the goal, allowing the system to maintain high decision accuracy while adapting computational resources to only process relevant information
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent interacting with an environment to accomplish a goal. In one aspect, a method comprises: generating a respective planning embedding corresponding to each of multiple experience tuples in an external memory, wherein each experience tuple characterizes interaction of the agent with the environment at a respective previous time step; processing the planning embeddings using a planning neural network to generate an implicit plan for accomplishing the goal; and selecting the action to be performed by the agent at the time step using the implicit plan.