On-Device Reinforcement Learning Architecture for Local Network Adaptation
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Solution Overview
Problem
Existing radio access networks face challenges in optimizing resource utilization and minimizing performance degradation due to the difficulty in gathering labeled data sets for supervised learning, making reinforcement learning (RL) an attractive alternative for adapting to local environmental characteristics.
Innovation Solution
An on-device reinforcement learning architecture comprising a machine learning management service, data collection engine, training engine, and inference engine, which supports both tabular RL and deep RL, enabling the training of neural networks using training data samples to learn value or policy functions for implementing functionalities in network apparatuses like base stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning is used to adapt to local environmental characteristics in radio access networks, then the ability to automatically adapt and optimize resource utilization is improved, but the difficulty in gathering labeled data sets and system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: a data collection engine that gathers training data, a training engine that processes the data and trains machine learning models, and an inference engine that executes the trained models. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by distributing complexity across specialized subsystems.
Solution Approach 2:
A machine learning management service acts as an intermediary between the reinforcement learning agent and the training infrastructure. This intermediary coordinates data collection, manages training processes, and handles model deployment, thereby reducing the complexity burden on the core RL agent while enabling sophisticated adaptive behavior.
2Productivity
If reinforcement learning is implemented in network apparatuses for automatic adaptation, then resource utilization optimization is improved, but the difficulty in gathering labeled data sets worsens
Solution Approach 1:
The data collection engine proactively collects training data samples from the radio access network environment before formal model training begins. It gathers state-action-reward tuples by having the RL agent interact with the environment, storing these samples in a replay buffer. This preliminary data collection ensures sufficient training data is available without requiring external labeled datasets.
Solution Approach 2:
The reinforcement learning system is self-sufficient in generating its own training data through environmental interactions. The data collection engine automatically captures state-action-reward samples during normal operation, eliminating the need for external labeled datasets. The system learns from its own experiences in the radio access network environment, making it adaptable to local characteristics without external data sources.
3Reliability
If exploration-exploitation balancing rules are adapted for effective decision-making, then the decision-making quality is improved, but the training time and convergence difficulty increase
Solution Approach 1:
The exploration-exploitation balancing rule is dynamically adjusted during training based on convergence conditions. The training engine monitors performance metrics and automatically modifies the exploration rate, transitioning from high exploration early in training to exploitation-dominated behavior as convergence is approached. This dynamic adaptation optimizes both training efficiency and final decision-making quality.
Solution Approach 2:
The training engine implements continuous feedback mechanisms by monitoring convergence conditions and using this information to adjust training parameters. When convergence is detected, the system reduces exploration and increases exploitation, thereby reducing unnecessary training time while maintaining high decision-making quality. The feedback loop ensures optimal balance between exploration and exploitation throughout the training process.
Data Source
AI summary
An architecture is disclosed to enable on-device reinforcement learning (RL) for implementing a functionality of an apparatus. The architecture includes a machine learning management service, a data collection engine, a training engine, and an inference engine. It cooperates with a RL agent which is part of the software implementing the functionality. The architecture supports tabular RL and deep RL. During training, the RL agent sends training data samples to the data collection engine and the training engine computes the state-action values/policy from the training data samples. In tabular RL, the training engine updates a table of state-action values. In deep RL, it constitutes a training data set from the training data samples and the computed targets and uses the training data set to train a neural network. When trained the neural network is provided to the inference engine for implementing the functionality.