Federated Reinforcement Learning for Multi-Device Control
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Solution Overview
Problem
Conventional device control systems face challenges in achieving precise control, especially when high precision is required, due to lengthy learning processes for building learning models, and individual reinforcement learning for each device takes a long time, especially when devices have similar characteristics.
Innovation Solution
The implementation of a federated reinforcement learning system that shares gradients and learning parameters across multiple device controllers, allowing for accelerated and precise control by averaging gradients and transferring learning parameters from completed to incomplete device controllers, thereby completing reinforcement learning faster and improving control accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reinforcement learning is performed individually for each device controller, then control precision is improved, but training time increases significantly
Solution Approach 1:
The patent merges reinforcement learning processes across multiple device controllers by sharing gradients and learning parameters. Device controllers perform local reinforcement learning and share gradients with a server, which aggregates them into global gradients. This combining approach allows controllers to benefit from collective learning experience, achieving precise control while reducing individual training time through parameter transfer from controllers that have completed training.
Solution Approach 2:
The patent implements preliminary action by transferring learning parameters from device controllers that have completed reinforcement learning to controllers that are still training. When a controller finishes training, its learned parameters are shared with other controllers, allowing them to start from a more advanced state and skip portions of the training process, thereby reducing overall training time while maintaining control precision.
2Measurement precision
If reinforcement learning is performed for each device, then device-specific control accuracy is improved, but overall system training efficiency deteriorates
Solution Approach 1:
The patent segments the reinforcement learning process into local and global components. Each device controller performs local reinforcement learning on its specific device to maintain device-specific control accuracy. Simultaneously, the system performs global reinforcement learning by aggregating gradients from all controllers and transferring learned parameters back to individual controllers. This segmentation allows parallel processing while maintaining specialization.
Solution Approach 2:
The patent implements universality through a centralized server that performs multiple functions: collecting gradients from all device controllers, aggregating them into global gradients, and distributing updated parameters back to controllers. This universal server enables the system to learn common patterns across multiple devices while allowing each controller to maintain device-specific adaptations, thereby improving overall training efficiency without sacrificing device-specific accuracy.
3Measurement precision
If more learning data is used for training, then learning model accuracy is improved, but training time increases
Solution Approach 1:
The patent implements feedback mechanisms where device controllers continuously share their learning progress and gradients with the central server. The server aggregates this feedback from multiple controllers and uses it to update global learning parameters, which are then fed back to individual controllers. This feedback loop allows the system to leverage data from multiple sources simultaneously, improving model accuracy without requiring each individual controller to process all data sequentially.
Data Source
AI summary
The present disclosure relates to a system and method for controlling multiple devices through a federated reinforcement learning, in more detail, in case of performing the reinforcement learnings for controlling each of a plurality of devices in each of the plurality of devices, provided a system and method for controlling multiple devices through the federated reinforcement learning to be able to precisely control the plurality of devices using the reinforcement learning result as well as to finish the reinforcement learning at high speed, by performing a coalition of the reinforcement learning in the plurality of device controllers, through a gradient sharing process shared with the plurality of device controllers by averaging the gradients for each of the reinforcement learnings and a learning parameter transfer process transferring the learning parameter of a particular device controller that a reinforcement learning is terminated first through the gradient sharing process to at least one device controller that the reinforcement learning is not completed yet.