Action Selection via Text and Observation Embeddings
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Solution Overview
Problem
Existing reinforcement learning systems struggle to effectively integrate natural language instructions with environmental observations to guide agent actions, lacking efficient methods to comprehend and execute written instructions in dynamic environments.
Innovation Solution
A system that combines a language encoder model, an observation encoder neural network, and an action selection neural network to process natural language instructions and environmental observations, generating a combined embedding to select appropriate actions for the agent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning systems use traditional methods to process environmental observations, then the system complexity remains manageable, but the ability to comprehend and execute natural language instructions is insufficient
Solution Approach 1:
The patent introduces embedding vectors as intermediary representations that bridge natural language instructions and environmental observations. The language encoder transforms text into embedding vectors, which are then combined with observation embeddings to guide action selection. This intermediary representation layer enables the system to comprehend and execute natural language instructions without requiring direct complex interactions between raw text and environmental data.
Solution Approach 2:
The system segments the processing of natural language instructions and environmental observations into separate encoding pathways. The language encoder processes text independently to generate embedding vectors, while the observation encoder processes environmental data separately. These segmented processing streams are then integrated through embedding combination, allowing the system to handle each input type with specialized processing while maintaining overall system manageability.
2Productivity
If reinforcement learning systems integrate natural language processing with environmental observations, then task performance improves, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary encoding of natural language instructions into embedding vectors before they are needed for action selection. The language encoder processes and stores the semantic meaning of instructions in advance as compact embedding representations. When action selection is required, these pre-computed embeddings are readily available for combination with current observations, eliminating the need for real-time text processing and reducing processing time during critical decision moments.
3Adaptability or versatility
If reinforcement learning systems use rule-based methods to execute instructions, then the system requires extensive prior knowledge and rules, but the flexibility to handle dynamic environments is reduced
Solution Approach 1:
The patent replaces rule-based mechanical systems with neural network-based learning systems. Instead of using hand-crafted rules and extensive prior knowledge to guide agent behavior, the system employs neural networks (language encoder, observation encoder, and policy network) that learn appropriate behaviors from data. The neural networks process natural language instructions and environmental observations to generate actions, eliminating the need for explicit rule programming while maintaining adaptability to dynamic environments.
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 interacting with an environment. In one aspect, a system includes a language encoder model that is configured to receive a text string in a particular natural language, and process the text string to generate a text embedding of the text string. The system includes an observation encoder neural network that is configured to receive an observation characterizing a state of the environment, and process the observation to generate an observation embedding of the observation. The system includes a subsystem that is configured to obtain a current text embedding of a current text string and a current observation embedding of a current observation. The subsystem is configured to select an action to be performed by the agent in response to the current observation.