Reward Autoencoder for Reinforcement Learning Network Policies
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Solution Overview
Problem
Defining rewards for reinforcement learning models in network performance management is challenging due to high-dimensional data and incorrect reward definitions, leading to wastage of computing and networking resources.
Innovation Solution
A reward autoencoder platform generates rewards by embedding network performance data into a lower-dimensional space, calculating reconstruction errors, and determining convex hulls to define optimal network policies and metrics, thereby improving network performance and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If reinforcement learning is used for network performance management, then automation and adaptability are improved, but difficulty in defining rewards and modeling actions increases
Solution Approach 1:
The system enables the reinforcement learning model to automatically define its own reward functions and action models based on observed network states and outcomes, eliminating the need for manual specification of rewards and actions by operators
Solution Approach 2:
An intermediary layer is introduced that automatically translates network performance data into meaningful reward signals and action representations, bridging the gap between raw data and reinforcement learning requirements
2Measurement precision
If high-dimensional network performance data is used, then measurement precision is improved, but loss of outlier detection and computational efficiency decreases
Solution Approach 1:
The system extracts and separates outlier data points from the main high-dimensional dataset, applying specialized processing to identify and flag anomalies while maintaining the integrity of the overall performance measurement
Solution Approach 2:
Outlier detection and handling is performed as a preliminary step before main reinforcement learning processing, ensuring that anomalous data points are identified and managed before they can interfere with normal operation
3Measurement precision
If manual reward definition is used, then control and precision are improved, but productivity and resource efficiency decrease
Solution Approach 1:
The reinforcement learning system automatically generates and refines its own reward definitions based on observed network performance patterns, eliminating manual intervention while maintaining adaptive precision
Solution Approach 2:
Reward definitions are made dynamic and adaptive, automatically adjusting to changing network conditions and performance requirements without manual reconfiguration
Data Source
AI summary
A device may receive network policies of a network, and network performance data identifying KPIs of the network, and may generate an embedded space of reconstructed data that is embedded in an original space that includes the KPIs. The device may calculate reconstruction errors based on differences between the reconstructed data and the network performance data, and may calculate a convex hull of the original space. The device may calculate a convex hull of the embedded space, and may determine reward metrics based on the reconstruction errors, the convex hull of the original space, and the convex hull of the embedded space. The device may define performance baselines associated with portions, and may generate a new reward for a portion based on a particular reconstruction error, a particular convex hull of the embedded space, and a particular performance baseline. The device may perform actions based on the new reward.