Learning Agents in Simulated Environments
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Solution Overview
Problem
Current simulation engines are unable to generate agents capable of machine learning within simulated environments, particularly those that are procedurally or stochastically generated, resulting in static and hard-coded behaviors that are not suitable for simulating real-world scenarios like economic or biological systems.
Innovation Solution
A system and method for creating and executing learning agents in simulated environments, allowing agents to utilize machine learning and evolve their behavior within stochastic and ever-changing environments, enabling the simulation of dynamic real-world situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If agents operate on hard-coded and static behaviors in simulated environments, then the simulation engine can run with pre-set agents achieving a variety of results, but the agents are not capable of machine learning techniques and cannot adapt to stochastic or procedurally generated environments
Solution Approach 1:
The patent transforms static, hard-coded agent behaviors into dynamic, learnable behaviors by implementing neural networks that can adapt and evolve during simulation runtime. Agents transition from fixed behavior trees to flexible neural network models that continuously learn from environmental interactions, enabling adaptation to stochastic and procedurally generated environments.
Solution Approach 2:
The system changes the fundamental parameters of agent behavior from static code-based decisions to dynamic neural network parameters that can be adjusted through machine learning. By modifying agent parameters from fixed values to learnable weights and biases, the system enables agents to adapt their behavior based on environmental feedback and experience.
2Reliability
If simulation environments are generated procedurally or stochastically to elucidate real-world situations, then the environments become more realistic and useful, but existing agents cannot learn and evolve their behavior in these changing environments
Solution Approach 1:
The patent implements feedback mechanisms where agents receive environmental feedback through sensors and update their neural network parameters based on observed outcomes. This closed-loop feedback system allows agents to learn from their interactions with stochastic environments, adjusting their behavior to maximize performance in realistic, dynamically generated scenarios.
Solution Approach 2:
Agents perform self-learning and self-adjustment through machine learning algorithms, eliminating the need for external reprogramming or manual behavior adjustment. The agents autonomously adapt to procedural and stochastic environments by learning from their own experiences and environmental feedback, enabling them to thrive in realistic, changing conditions.
3Adaptability or versatility
If agents use machine learning techniques to learn and evolve behavior, then agents can adapt to stochastic environments and simulate real-world situations, but the system requires integration of neural networks with simulation engines which increases complexity
Solution Approach 1:
The patent creates a universal agent framework that can operate across multiple simulation environments and domains. By designing neural network-based agents with standardized interfaces and behavior patterns, the system achieves multi-functionality where the same agent architecture can adapt to various stochastic and procedural environments, reducing overall system complexity through standardization.
Solution Approach 2:
The system introduces intermediary components that bridge neural network intelligence with simulation engine operations. These intermediary layers handle the complexity of integrating machine learning with traditional simulation, providing standardized interfaces for sensor input, actuator output, and environment interaction, thereby managing integration complexity while enabling advanced agent capabilities.
Data Source
AI summary
A system and methods for generating and applying learning agents in simulated environments, in which an agent simulation is selected, one or more agent goals are received, and agents are created which are individual instances of the agent simulation with each agent having at least one of the agent goals, wherein the agents are used in the execution of an environment simulation which dynamically changes based on the collective behavior of the agents.