Resource-Aware Goal-Driven Policy Reinforcement Learning (RAGP-RL)
Resource-Aware Goal-Driven Policy Reinforcement Learning (RAGP-RL)
This technical report introduces RAGP-RL, a conceptual framework for reinforcement learning that explicitly integrates computational resource constraints into policy-level decision making. The work focuses on high-level formulation and theoretical structure rather than implementation details. No source code, trained models, hyperparameters, or experimental benchmarks are provided in this publication. Practical implementations and empirical evaluations are intentionally omitted. This publication is intended as a defensive disclosure to establish prior art for the core concepts described herein and to support future research and discussion.
Artificial Intelligence, Reinforcement learning, Reinforcement Learning Resource-Aware Learning Policy Optimization Energy-Efficient AI Computational Constraints AI Systems
Artificial Intelligence, Reinforcement learning, Reinforcement Learning Resource-Aware Learning Policy Optimization Energy-Efficient AI Computational Constraints AI Systems
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