Distributed Reinforcement Learning for Neural Network Environment Control
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Solution Overview
Problem
Current environment control systems in buildings face challenges in efficiently generating and improving predictive models for neural networks used in environmental control, requiring large datasets and manual automation during the training phase.
Innovation Solution
A training server that receives data from multiple environment controllers, determines reinforcement signals based on executed commands, and updates neural network weights to improve predictive models through distributed reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a predictive model is generated during training phase using neural networks, then environmental control accuracy is improved, but a large number of samples are required which increases training time and data requirements
Solution Approach 1:
The patent implements feedback mechanisms where environment controllers send execution results and metrics back to the training server. The training server uses this feedback to iteratively update the predictive model weights, improving accuracy progressively without requiring all training samples to be processed upfront. This allows the system to achieve high accuracy with fewer initial samples by continuously learning from real-world execution feedback.
2Productivity
If the predictive model is updated using distributed reinforcement learning, then model improvement efficiency is enhanced, but system complexity increases due to multiple environment controllers and training server coordination
Solution Approach 1:
The system is segmented into distinct functional components: multiple environment controllers that collect and transmit data, a training server that processes data and updates models, and communication interfaces that facilitate data exchange. This segmentation allows parallel data collection from multiple controllers while centralizing the complex model updating logic, thereby improving efficiency without overwhelming any single component with excessive complexity.
Solution Approach 2:
The training server acts as an intermediary between environment controllers and the central model storage. It receives training data from multiple controllers, processes the reinforcement learning updates, and distributes the updated predictive model weights back to all controllers. This intermediary role simplifies the coordination complexity by providing a centralized processing point rather than requiring direct peer-to-peer communication between all system components.
3Extent of automation
If automation is introduced in the training process through distributed reinforcement learning, then training process efficiency is improved, but the complexity of implementing and managing the automated system increases
Solution Approach 1:
The system implements self-service automation where environment controllers autonomously collect training data from their local environments, automatically transmit it to the training server, and receive updated model weights without manual intervention. The training server automatically processes the received data, updates the predictive model using reinforcement learning algorithms, and redistributes the improved model to all controllers. This self-service approach maximizes automation while managing complexity through clear role assignment to each system component.
Data Source
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AI summary
Interactions between a training server and a plurality of environment controllers are used for updating the weights of a predictive model used by a neural network executed by the plurality of environment controllers. Each environment controller executes the neural network using a current version of the predictive model to generate outputs based on inputs, modifies the outputs, and generates metrics representative of the effectiveness of the modified outputs for controlling the environment. The training server collects the inputs, the corresponding modified outputs, and the corresponding metrics from the plurality of environment controllers. The collected inputs, modified outputs and metrics are used by the training server for updating the weights of the current predictive model through reinforcement learning. A new predictive model comprising the updated weights is transmitted to the environment controllers to be used in place of the current predictive model.