Dynamic Reinforcement Learning Simulation for Resource Allocation
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Solution Overview
Problem
Real-life environments for reinforcement learning are often more complex than simulated environments, making it difficult for reinforcement learning agents to learn policies that account for dynamic and varying responses to resource allocations over time.
Innovation Solution
A computing system that includes a reinforcement learning agent model and an entity model to simulate resource allocations and responses in a dynamic environment, allowing the agent to learn policies that prioritize long-term benefits while experiencing short-term negative effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning agents are trained in simple simulated environments, then training efficiency is improved, but the agent's ability to handle complex real-life environments deteriorates
Solution Approach 1:
The patent implements dynamic entity profiles that evolve over time through state transitions. Entities have profiles that change based on their current state, allowing the simulation environment to dynamically adapt its complexity. This enables agents to train in environments that become increasingly complex, bridging the gap between simple training environments and complex real-life scenarios.
Solution Approach 2:
The patent pre-generates transition probabilities and entity profiles before the reinforcement learning training begins. This preliminary preparation creates a structured yet complex environment that can be efficiently simulated during training, allowing agents to learn from pre-computed environmental dynamics without sacrificing training speed.
2Adaptability or versatility
If simulated environments are made more complex to match real-life scenarios, then agent adaptability is improved, but computational complexity and training time increase
Solution Approach 1:
The patent segments the complex environment into discrete entities with individual profiles and state transitions. Each entity is modeled separately with its own transition probability matrix, allowing the overall complex environment to be composed of manageable, independent components that can be efficiently simulated and updated.
Solution Approach 2:
The patent uses parameterized entity profiles that capture essential characteristics of entities in the environment. By representing complex entity behaviors through a limited set of parameters (state, transition probabilities, resource consumption), the system maintains computational efficiency while still modeling complex real-life scenarios.
3Productivity
If static entity profiles are used in simulation, then computational efficiency is improved, but the ability to model dynamic real-life responses deteriorates
Solution Approach 1:
The patent implements dynamic entity profiles where entities have current states that evolve through state transitions. The transition probability matrices capture how entities dynamically respond to resource allocations and environmental changes, allowing the simulation to efficiently model temporal dynamics and varying responses over time.
Data Source
AI summary
A computing system for simulating allocation of resources to a plurality of entities is disclosed. The computing system can be configured to input an entity profile that describes a preference and/or demand of a simulated entity into a reinforcement learning agent model and receive, as an output of the reinforcement learning agent model, an allocation output that describes a resource allocation for the simulated entity. The computing system can select one or more resources based on the resource allocation described by the allocation output and provide the resource(s) to an entity model that is configured to simulate a simulated response output that describes a response of the simulated entity. The computing system can receive, as an output of the entity model, the simulated response output and update a resource profile that describes the at least one resource and/or the entity profile based on the simulated response output.