Cloud Server for Reinforcement Learning Resource Allocation
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Solution Overview
Problem
Reinforcement learning systems face limitations in learning efficiency and practicality due to resource constraints and the inability to reuse or update architectures for different learning objectives, restricting their scope of application.
Innovation Solution
A reinforcement learning system and method that utilizes cloud-based hardware resources for training, featuring a modular design allowing for easier reuse and update of the framework, enabling adaptation for various agents and environments through a server with a connection unit, storage unit, and processing unit, including untrained and trained model pools, and a sampling calculation model for selecting optimal resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning is performed on agent-side hardware, then the system can operate autonomously, but learning efficiency is limited by resource constraints
Solution Approach 1:
The patent introduces a cloud-based server as an intermediary between the agent and training resources. The server handles computationally intensive training tasks while the agent performs lightweight inference locally. This mediator architecture allows the system to leverage powerful remote hardware resources without requiring the agent to have substantial local computing power, thereby resolving the contradiction between learning efficiency and hardware resource constraints.
Solution Approach 2:
The patent transitions the training process from a local single-dimension operation to a distributed cloud-based multi-dimensional operation. By moving training to the cloud, the system accesses additional dimensions of computational power, storage, and parallel processing capabilities that are unavailable on edge devices, effectively resolving the resource limitation while maintaining high learning efficiency.
2Reliability
If reinforcement learning architecture is customized for each learning objective, then the system can achieve optimal performance, but the scope of application and practicality are reduced
Solution Approach 1:
The patent implements a universal reinforcement learning platform that can handle multiple learning objectives through a standardized architecture. The system uses configurable reward functions, adjustable hyperparameters, and modular training pipelines that can be adapted to different tasks without requiring complete redesign. This universal framework maintains optimal performance across diverse applications while significantly expanding the scope of practical use cases.
Solution Approach 2:
The patent introduces dynamic configurability to the reinforcement learning architecture, allowing the system to adapt its parameters, reward structures, and training strategies based on the specific learning objective. This dynamic adaptation mechanism enables the same core architecture to optimize performance for different tasks by adjusting its behavior and configuration, thereby achieving both high reliability and broad adaptability.
3Ease of manufacture
If fixed reinforcement learning architecture is used, then the system is easy to implement, but it cannot be reused or updated for different learning objectives
Solution Approach 1:
The patent segments the reinforcement learning system into modular components including separate reward function modules, training pipeline stages, and configurable parameter sets. Each module can be independently configured, updated, or replaced based on learning objectives. This segmentation maintains implementation simplicity through standardized interfaces while enabling extensive reusability and adaptability across different applications.
Data Source
AI summary
A reinforcement learning system is provided, including at least one agent and a server. According to a set condition, the at least one agent transmits a plurality of state sets related to a state of the environment through a network, receives a plurality of action sets for performing an action, and transmits, to the server, a plurality of feedback messages generated after interacting with the environment. The server configures a predetermined ratio of the memory space as at least one workstation according to the set condition, and selects an untrained model to be temporarily stored in the at least one workstation. The at least one workstation imports a current state set, a current action set, and a current feedback message, as parameters, into the untrained model for reinforcement learning, and generates a next action set until a goal is achieved.