Compositional Reinforcement Learning With Attention for Task Generalization
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Solution Overview
Problem
Reinforcement learning systems face challenges in effectively controlling agents to perform tasks, especially in environments with complex observations like pixel data or tasks requiring interaction with multiple entities, often requiring extensive training and memory resources.
Innovation Solution
A reinforcement learning system that uses an encoder neural network and subschema recurrent neural networks to process observations, generate attention-weighted feature vectors, and update hidden states to select actions, allowing for improved task performance and generalization across similar tasks without the need for explicit task definitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning systems use traditional neural networks to control agents in complex environments, then the agent can process observations and select actions, but the training time and memory resources required become excessively large
Solution Approach 1:
The patent segments the observation processing into multiple subschema recurrent neural networks, each handling specific aspects of the environment. This segmentation allows parallel processing of different observation components, reducing overall training time and memory requirements while maintaining comprehensive environment understanding.
Solution Approach 2:
The patent introduces attention mechanisms that add a new dimension to feature processing by dynamically weighting feature importance. This attention dimension allows the system to focus computational resources on critical features rather than uniformly processing all features, significantly improving training efficiency in complex environments.
2Adaptability or versatility
If reinforcement learning systems use traditional neural networks for task control, then the system can learn from interactions, but it requires extensive memory resources and fails to generalize to unseen tasks
Solution Approach 1:
The patent creates a universal agent architecture with subschema recurrent neural networks that can handle multiple task types through a unified framework. This universal structure allows the agent to generalize across different tasks by learning common patterns, reducing the need for task-specific memory while maintaining adaptability to new situations.
Solution Approach 2:
The patent employs attention mechanisms that dynamically change parameter weights based on input observations. This dynamic parameter adjustment allows the system to adapt to different task requirements without storing extensive task-specific information, achieving generalization through flexible parameter modulation rather than memorization.
3Measurement precision
If reinforcement learning systems process complex observations like pixel data, then the agent can perceive detailed environment states, but the computational complexity and training resources required increase significantly
Solution Approach 1:
The patent extracts and processes only the most relevant features from complex observations using attention mechanisms. By selectively extracting important features rather than processing all pixel data uniformly, the system maintains high observation accuracy while reducing computational complexity and training resource requirements.
Solution Approach 2:
The patent implements dynamic feature processing where the importance of different observation components changes based on the current state and task requirements. This dynamic approach allows the system to adaptively allocate computational resources to the most relevant features, maintaining precision while managing complexity.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling an agent interacting with an environment to perform a task. In one aspect, one of the methods comprises receiving an observation; processing the observation using an a recurrent encoder neural network configured to receive as input the observation and to generate as output an encoder representation of the observation that comprises a respective feature vector for each of a plurality of spatially distinct portions of the observation, wherein each respective feature vector has a plurality of dimensions; for each of a plurality of subschema recurrent neural networks: generating a respective attention weight for each of the plurality of dimensions, generating an attended encoder representation, and updating the subschema hidden state using at least the attended encoder representation; and selecting an action using the updated subschema hidden states of the plurality of subschema recurrent neural networks.