GPU-Parallel Virtual Environment Training for Scalable AI Agents
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Solution Overview
Problem
The existing method of training AI agents in video games using CPU-based computation is limited by the number of CPU cores and threads, leading to reduced simulation speed and scalability, and is laborious and time-consuming to build a complete game environment.
Innovation Solution
Utilizing a graphics processing unit (GPU) for parallel thread execution and sharing static data across multiple environments, allowing for efficient calculation of parameter variations and subsequent parameters, and employing deep learning to derive agent actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computation is implemented on CPU with independent threads for each agent, then each agent can be simulated independently, but the number of agents is limited by CPU cores and threads, leading to reduced simulation speed and scalability
Solution Approach 1:
The patent replaces the CPU-based thread execution mechanism with GPU-based parallel processing. Instead of using CPU cores and threads to simulate agents sequentially or with limited parallelism, the invention leverages the GPU's massive parallel architecture to simultaneously process thousands of agents, thereby resolving the contradiction between simulation speed and scalability
Solution Approach 2:
The patent changes the fundamental parameter of computational architecture from CPU-thread based to GPU-parallel based. This parameter change enables the system to handle a much larger number of agents simultaneously, improving both simulation speed and scalability without being constrained by CPU core limitations
2Reliability
If a complete game environment is built for training, then agents can interact realistically, but the process is laborious and time-consuming
Solution Approach 1:
The patent creates simplified virtual environment copies that replicate only the essential elements needed for agent training. Instead of building complete game environments with all assets, textures, and complex interactions, the invention uses streamlined virtual representations that maintain training effectiveness while dramatically reducing setup time and computational overhead
Solution Approach 2:
The patent extracts and isolates only the critical components necessary for agent training from the complete game environment. By separating the essential training elements (agent interactions, state transitions, rewards) from non-essential game features, the system achieves reliable training results without the laborious process of building entire game worlds
Data Source
AI summary
A method for training a model in a virtual environment, a medium, an electronic device, and a computer program product. The method includes: an obtaining step, obtaining one or more initial parameters of each of one or more agents in the virtual environment, and causing each of one or more agents to generate one or more actions; an updating step, causing each agent to simultaneously perform the one or more actions in the virtual environment, so as to calculate, for each agent, one or more parameter variations that are respectively in one-to-one correspondence with the one or more initial parameters, and updating, based on the one or more initial parameters and the one or more parameter variations, to obtain one or more subsequent parameters of each agent in the virtual environment. It enhances scalability and reusability of simulating a game environment, increases the speed of simulating the game environment, and overall reduces the simulation time.