Asynchronous Reinforcement Learning for Decision Latency
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Solution Overview
Problem
Conventional Reinforcement Learning (RL) systems are limited in their ability to handle asynchronous decision-making processes, particularly in scenarios with multiple users and delayed feedback, leading to inefficiencies and long delays in model updates.
Innovation Solution
The implementation of an asynchronous Reinforcement Learning (RL) system that allows for asynchronous generation of decisions and updates to the value function, independent of user activity, using a time-dependent value function and policy updates triggered by timers or schedules, enabling efficient decision-making and model updates in dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional Reinforcement Learning systems are used, then model updates can be performed, but decision-making delays increase and responsiveness to dynamic environments deteriorates
Solution Approach 1:
The patent implements asynchronous Reinforcement Learning where the value function and policy are updated dynamically in response to user interactions rather than through fixed periodic cycles. This allows the model to adapt real-time to changing user behaviors and preferences, eliminating delays associated with batch processing and enabling immediate response to new data.
Solution Approach 2:
The system maintains continuous learning by updating the value function and policy continuously as users interact with the service, rather than performing updates periodically. This continuous update mechanism ensures that the model is always current with the latest user data, eliminating gaps in knowledge and reducing response time for decision-making.
2Stability of the object's composition
If synchronous model updates are performed, then consistency is maintained, but adaptability to asynchronous user interactions deteriorates
Solution Approach 1:
The patent employs dynamic updates to the value function and policy that occur asynchronously with user interactions. The model consistently incorporates new user data immediately upon interaction, maintaining both consistency through continuous learning and adaptability through real-time updates, rather than waiting for synchronous processing cycles.
Solution Approach 2:
The system implements feedback mechanisms where user interactions directly trigger model updates. This feedback loop ensures that the model continuously adapts to user behaviors and preferences in real-time, maintaining consistency through systematic update procedures while achieving high adaptability to asynchronous user patterns.
3Use of energy by stationary object
If batch processing is used for model updates, then resource consumption is reduced, but responsiveness to dynamic environments deteriorates
Solution Approach 1:
The patent implements continuous learning where the model updates continuously as users interact with the service, rather than performing periodic batch updates. This continuous update mechanism maintains high productivity by immediately incorporating new data while managing computational resources efficiently through event-driven processing that activates only when user interactions occur.
Solution Approach 2:
The system performs self-updating through asynchronous Reinforcement Learning, where the model automatically adjusts its value function and policy in response to user interactions without requiring external batch processing intervention. This self-service approach maintains high decision-making productivity while optimizing resource consumption by processing only when necessary.
Data Source
AI summary
In one embodiment, an indication that a decision has been requested, selected, or applied with respect to one or more users may be obtained. After the indication that a decision that has been requested, selected, or applied is obtained, a value function may be updated, where the value function approximates an expected reward associated with the one or more users over time since the decision has been requested, selected, or applied with respect to the one or more users. The value function may be updated by performing or providing one or more updates to the value function, where a time at which each of the one or more updates is performed or provided is independent of activity of the one or more users.