Simulation Management Service for Reinforcement Learning

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

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 the evaluation and modification of these elements to facilitate the training of reinforcement learning models, with separate software container instances for simulation and training, enabling asynchronous communication and real-time monitoring.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

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

Engineering Contradiction:

Improvereinforcement learning model training speedVSAvoidtime required to configure agent and define reinforcement learning function

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by automatically generating the agent configuration and reinforcement learning function definitions before the actual training process begins. The simulation environment is pre-configured with necessary parameters, and the reinforcement learning function is automatically created based on the simulation setup, eliminating the need for manual configuration during the training phase.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables self-service by allowing the simulation environment to automatically generate and configure the reinforcement learning components without requiring manual intervention. The simulation framework itself performs the configuration tasks, reducing developer involvement and accelerating the model training process.

Inventive Principle:

Principle #25Self-service

2Reliability

If developers manually program the agent for exploration, then the agent can perform reinforcement learning, but the process becomes error-prone and time-intensive

Engineering Contradiction:

Improveaccuracy of agent configurationVSAvoidcomplexity of agent programming

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system extracts the complex agent programming tasks from manual developer work and transfers them to automated system generation. The configuration of exploration parameters, agent behavior, and reinforcement learning functions is automatically extracted and generated by the simulation framework, reducing both errors and programming complexity.

Inventive Principle:

Principle #2Taking out (Extraction)

3Adaptability or versatility

If traditional simulation environments are used, then reinforcement learning can be performed, but the flexibility to create custom models is limited

Engineering Contradiction:

Improveflexibility in creating custom reinforcement learning modelsVSAvoidcomplexity of simulation environment setup

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system implements dynamics by making the simulation environment configurable and adaptable through automated processes. The simulation framework allows dynamic creation of custom reinforcement learning models with flexible parameters that can be adjusted without requiring complex manual setup, enabling versatility while maintaining simplicity through automation.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS11836577B2Reinforcement learning model training through simulation

Publication Date: 2023.12.05 AMAZON TECH INC

AI summary

A simulation management service receives a request to perform reinforcement learning for a robotic device. The request can include computer-executable code defining a reinforcement function for training a reinforcement learning model for the robotic device. In response to the request, the simulation management service generates a simulation environment and injects the computer-executable code into a simulation application for the robotic device. Using the simulation application and the computer-executable code, the simulation management service performs the reinforcement learning within the simulation environment.