# A Computer Chip for Training Many AI Agents at Once > This patent describes a specialized computer processor designed to quickly train multiple artificial intelligence agents simultaneously using a unique set of instructions, speeding up how AI learns. - **Patent:** US 9754221 - **Original title:** Processor for implementing reinforcement learning operations - **Owner:** Alphaics - **Granted:** 2017 - **Status:** Active - **Times cited:** 53 - **Field:** ai_ml, semiconductors, consumer_electronics, software, telecommunications ## What it does The system uses a "first processor" to set up AI "reinforcement learning agents" and their "environments," assigning each a unique ID (Claim 1). A "first memory module" stores special instructions, part of an "application-domain specific instruction set (ASI)," which include these agent or environment IDs as "operands" (Claim 1). A "complex instruction fetch and decode (CISFD) unit" then decodes these instructions, creating "threads" that also carry the IDs (Claim 1). A "second processor" with multiple "cores" executes these threads in parallel, applying instructions to the correct agents or environments (Claim 1). This second processor determines actions, "state-value functions," "Q-values," and "reward values" for the agents. These values are stored in a "second memory module" and then used to train a "neural network" via a "neural network data path" to improve the AI's learning (Claim 1). For example, a system could train hundreds of robotic agents to navigate different simulated environments simultaneously using these specialized instructions. ## What it does NOT cover - General-purpose computer processors running reinforcement learning algorithms without specialized instruction sets. - Reinforcement learning systems that do not use agent or environment IDs as operands within their instructions. - Hardware architectures that do not separate the creation of agents/environments from the execution of learning operations into distinct processors. - Reinforcement learning implementations that do not communicate with a neural network via dedicated data paths for approximating reward or state-value functions. - Systems where multiple agents are processed sequentially rather than in parallel using "Single Instruction Multiple Agents (SIMA)" type instructions. ## The clever bit The core innovation is the "Single Instruction Multiple Agents (SIMA)" concept, where a single instruction can be applied simultaneously to many reinforcement learning agents interacting with their environments. This, combined with a specialized processor architecture and instruction set, allows for highly parallel and efficient training of AI. ## Real-world examples 1. Google's DeepMind AlphaGo and AlphaStar training systems 2. NVIDIA's specialized AI accelerators for robotics simulation 3. Robotics training platforms 4. Autonomous vehicle simulation environments ## Why it matters As artificial intelligence becomes more complex, training AI models, especially through reinforcement learning, requires immense computing power. This patent addresses this challenge by proposing dedicated hardware and instruction sets. This approach can significantly speed up the development and deployment of AI systems by making the learning process more efficient. ## Frequently asked questions ### What does A Computer Chip for Training Many AI Agents at Once cover? This patent describes a specialized computer processor designed to quickly train multiple artificial intelligence agents simultaneously using a unique set of instructions, speeding up how AI learns. ### Who owns patent US 9754221? Alphaics owns this patent, granted in 2017. ### When does this patent expire? This patent is expected to expire on March 9, 2037, when the invention enters the public domain. ### What is patent US 9754221 cited by? This patent has been cited by 53 later patents that build on its ideas. ### What problem does this patent solve? As artificial intelligence becomes more complex, training AI models, especially through reinforcement learning, requires immense computing power. This patent addresses this challenge by proposing dedicated hardware and instruction sets. This approach can significantly speed up the development and deployment of AI systems by making the learning process more efficient. ### What does this patent NOT cover? General-purpose computer processors running reinforcement learning algorithms without specialized instruction sets. **Full plain-English explainer:** https://patentbrief.org/patent/us/9754221/processor-for-implementing-reinforcement-learning-operations **Original patent:** https://patents.google.com/patent/US9754221 --- _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: - [How AI Agents Learn Faster by Predicting Opponents in Parallel](https://patentbrief.org/patent/us/11295174/opponent-modeling-with-asynchronous-methods-in-deep-rl) — This patent describes a computer system that trains artificial intelligence agents more efficiently by using many processors to simultaneously learn from a simulated environment, while also explicitly predicting the actions of other agents. - [Training Robot AI Models Faster Using Smart Simulations](https://patentbrief.org/patent/us/11836577/reinforcement-learning-model-training-through-simulation) — 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. - [How Computers Train AI Models Using Separate Virtual Simulations](https://patentbrief.org/patent/us/11429762/simulation-orchestration-for-training-reinforcement-learning-models) — This patent describes a system where one virtual computer runs simulations of a system, like a robot, and another virtual computer uses the simulation data to teach an AI model how to make better decisions. - [How AI Learns to Run Faster on Specific Computer Chips](https://patentbrief.org/patent/us/20250371349/methods-and-apparatus-for-hardware-aware-machine-learning-model-training) — This patent describes how a smart AI system, called a reinforcement learning agent, trains other AI models to run more efficiently on specific computer hardware by cleverly reducing their size without losing too much accuracy. - [How AI Models Learn Complex Tasks by Breaking Them Down](https://patentbrief.org/patent/us/11120365/for-hierarchical-decomposition-deep-reinforcement-learning-for-an-artificial-int) — This patent describes a method for training artificial intelligence models to solve big problems by splitting them into smaller, manageable steps and learning each step separately before combining them.