# Training Robot AI Models Faster Using Smart Simulations > This patent describes a cloud service that helps train artificial intelligence models for robots by running simulations, even suggesting improvements to the AI's learning rules before starting. - **Patent:** US 11836577 - **Original title:** Reinforcement learning model training through simulation - **Owner:** Amazon Technologies - **Granted:** 2023 - **Status:** Active - **Times cited:** 0 - **Field:** ai_ml, robotics, software, telecommunications, consumer_electronics ## What it does This patent details a computer-implemented method where a 'simulation management service' receives code from a customer. This code defines a 'reinforcement function' for training an AI model for a system, like a robot (Claim 1). The service then evaluates this code and suggests ways to improve it, based on past experiences with similar code (Claim 1). After modifying the code, the service creates a 'simulation environment' and injects the improved code into a 'simulation application' for the robot (Claim 1). Finally, it performs the reinforcement learning within this simulated world. For example, the simulation might select a robot's 'state' (like its position) and 'actions' (like moving forward), then provide a 'reward value' based on how well the action performed, which helps the AI model learn and improve (Claim 2, 4). ## What it does NOT cover - Does not cover training reinforcement learning models directly on physical robots without using a simulation environment. - Does not cover systems that train AI models without first evaluating and suggesting modifications to the customer's reinforcement function code. - Does not cover other types of machine learning, like supervised or unsupervised learning, that do not involve a reinforcement function and reward-based training. - Does not cover a simulation system where the user's code is not injected into a pre-existing simulation application. - Does not cover a system that doesn't use prior code or historical data to generate suggestions for modifying the reinforcement function. ## The clever bit The truly clever part is the 'simulation management service' automatically evaluating the customer's reinforcement function code and suggesting modifications based on prior data. This proactive optimization helps ensure the AI model learns more efficiently and effectively before the simulation even begins. ## Real-world examples 1. Amazon Web Services (AWS) RoboMaker 2. Cloud-based robotics simulation platforms 3. Autonomous vehicle training simulators 4. Industrial automation robot training 5. Logistics and warehouse robot pathfinding optimization ## Why it matters Training complex AI models for robots or autonomous systems is difficult and expensive in the real world. This patent matters because it provides a structured, cloud-based way to accelerate this training in a safe, virtual environment. By automatically suggesting improvements to the learning code, it helps developers create more effective AI models faster, reducing development costs and time for applications like warehouse automation or self-driving vehicles. ## Frequently asked questions ### What does Training Robot AI Models Faster Using Smart Simulations cover? This patent describes a cloud service that helps train artificial intelligence models for robots by running simulations, even suggesting improvements to the AI's learning rules before starting. ### Who owns patent US 11836577? Amazon Technologies owns this patent, granted in 2023. ### When does this patent expire? This patent is expected to expire on November 27, 2038, when the invention enters the public domain. ### What problem does this patent solve? Training complex AI models for robots or autonomous systems is difficult and expensive in the real world. This patent matters because it provides a structured, cloud-based way to accelerate this training in a safe, virtual environment. By automatically suggesting improvements to the learning code, it helps developers create more effective AI models faster, reducing development costs and time for applications like warehouse automation or self-driving vehicles. ### What does this patent NOT cover? Does not cover training reinforcement learning models directly on physical robots without using a simulation environment. **Full plain-English explainer:** https://patentbrief.org/patent/us/11836577/reinforcement-learning-model-training-through-simulation **Original patent:** https://patents.google.com/patent/US11836577 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [Nvidia's Method for Training Self-Driving Car AI in Simulations](https://patentbrief.org/patent/us/11436484/alphazero) — Nvidia's 2022 patent describes how to train AI for self-driving cars by using simulated environments and virtual sensors, then matching the simulated data format to real-world sensor data for AI processing. - [How AI Agents Learn to Pick the Best Future Actions](https://patentbrief.org/patent/us/10282665/action-selection-with-a-reward-estimator-applied-to-machine-learning) — A method for an AI agent to predict which actions will yield the highest rewards by analyzing past experiences and refining its decision-making model. - [Training AI on Private Data Without Seeing It](https://patentbrief.org/patent/us/12518214/distributed-machine-learning-systems-including-generation-of-synthetic-data) — This patent describes a way to train artificial intelligence models using private data stored on many separate computers, by generating fake data that mimics the real data's patterns, so the private data itself never leaves its original location. - [How AI Learns to Control Game Characters Based on Their Surroundings](https://patentbrief.org/patent/us/10607134/artificially-intelligent-systems-devices-and-methods-for-learning-andor-using-an-avatars-circumstances-for-autonomous-avatar-operation) — A system that allows digital characters to automatically perform actions by matching their current environment to previously learned experiences stored in a database. - [Making AI Smarter by Focusing on Unsure 'Nodes'](https://patentbrief.org/patent/us/12423586/training-nodes-of-a-neural-network-to-be-decisive) — This 2025 patent from D5AI LLC describes a way to train AI models more effectively by boosting the learning signal for 'nodes' that aren't making clear decisions on data.