Simulation Orchestration for Reinforcement Learning Training
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Solution Overview
Problem
Developers face challenges in creating virtual simulation environments for reinforcement learning, as configuring agents for exploration and defining reinforcement learning functions can be time-intensive and prone to errors, requiring significant trial and error.
Innovation Solution
A simulation management service provides a graphical user interface for customers to define simulation environments, parameters, and custom-designed reinforcement functions, allowing for rapid training of reinforcement learning models through separate software container instances and asynchronous communication, enabling real-time monitoring and flexible model development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If developers manually configure agents and define reinforcement learning functions, then the reinforcement learning process can be performed, but the development time and complexity increase significantly
Solution Approach 1:
The patent uses template definitions to create reusable configurations for simulation environments, agent behaviors, and reinforcement learning functions. Instead of manually configuring each component from scratch, developers can copy and adapt pre-defined templates, significantly reducing development time while maintaining correctness through proven template designs.
Solution Approach 2:
The system performs preliminary configuration by pre-defining simulation environment parameters, agent templates, and reinforcement learning function structures before the actual reinforcement learning process begins. This advance preparation eliminates the need for time-consuming manual configuration during the learning process itself.
2Ease of operation
If developers manually program agent exploration behavior, then the agent can perform exploration, but the configuration process becomes time-intensive and error-prone
Solution Approach 1:
The patent provides pre-defined agent templates that contain standard exploration behaviors and configurations. Developers can copy these templates and modify them as needed, rather than programming exploration behavior from scratch. This reduces both the time required and the potential for errors in the configuration process.
Solution Approach 2:
The system automatically generates and configures agent parameters based on the selected templates and simulation environment, reducing the need for manual programming. The agent configuration process becomes more self-service oriented, where the system handles much of the complex parameter setting automatically.
3Productivity
If custom reinforcement learning functions are defined through trial and error, then the reinforcement learning can be optimized, but the development process becomes extremely time-consuming
Solution Approach 1:
The patent provides pre-defined reinforcement learning function templates that have been optimized through prior experience and research. Developers can copy these proven templates and adapt them to their specific needs, avoiding the time-consuming trial and error process of creating reinforcement learning functions from scratch.
Solution Approach 2:
The system performs preliminary optimization by pre-configuring reinforcement learning functions with appropriate parameters and structures before the actual training begins. This advance setup eliminates the need for extensive trial and error tuning during the development process.
4Adaptability or versatility
If virtual simulation environments are created from scratch, then the environment can be customized, but the creation process becomes difficult and time-intensive
Solution Approach 1:
The patent segments the simulation environment into modular components that can be independently configured and combined. Instead of creating a monolithic environment from scratch, developers can assemble environments using discrete, pre-defined elements, reducing complexity while maintaining customization flexibility.
Solution Approach 2:
The system provides universal template definitions that can be applied across different simulation scenarios. These templates are designed to be multi-functional, allowing the same base templates to be adapted for various environments and use cases, reducing the need to create everything from scratch.
Data Source
AI summary
A simulation workflow manager obtains a set of parameters for simulation of a system and training of a reinforcement learning model for optimizing an application of the system. In response to obtaining the set of parameters, the simulation workflow manager configures a first compute node that includes a training application for training the reinforcement learning model. The simulation workflow manager also configures a second compute note with a simulation application to perform the simulation of the system in a simulation environment. Data is generated through execution of the simulation in the second compute node that is provided to the first compute node to cause the training application to use the data to train the reinforcement learning model.