Resource-Aware Goal-Driven Policy Reinforcement Learning (RAGP-RL)

By early 2026, global AI data center energy consumption is projected to reach 1,050 TWh, creating significant sustainability challenges for the implementation of large-scale intelligent agents. This paper proposes Resource-Aware Goal-Driven Policy Reinforcement Learning (RAGP-RL), a formal framework that explicitly integrates computational power constraints into the agent's objective function.

Unlike traditional Reinforcement Learning (RL) algorithms, RAGP-RL introduces the Imagination variable (I) as an internal generative process constrained by an energy budget (C). Through a Primal–Dual Lagrangian optimization formulation, it is shown how an agent can modulate its cognitive intensity based on the urgency of the situation (c) to achieve metabolic efficiency that mimics biological systems. Validation is proposed through Red vs. Blue adversarial simulations to measure the systemic efficiency and robustness of agents under resource-constrained conditions.

The RAGP-RL framework is defined by six key variables:

• Computational Power (C) : the energy capacity or processing resources available to the system.
• Imagination (I) : a generative stochastic process based on a world model to simulate future trajectories without direct interaction with the environment.
• Reality (R) : data resulting from actual interactions with the environment.
• Urgency Cost (c) : an adaptive scalar function that reflects the criticality of a situation.
• Decrease Function (d) : the rate of resource depletion or degradation.
• Direction (D) : a global goal priority vector that constrains the policy space when an agent is in a critical energy state.