Intelligent Task Offloading and Energy Management for Battery-Less 6G Industrial IoT Using Multi-Agent Deep Reinforcement Learning
TL;DR
A constrained, risk-sensitive multi-agent reinforcement learning framework is presented for joint task offloading and EH scheduling in battery-less 6G industrial networks, yielding a Pareto-non-dominated, statistically validated policy.
Abstract
Battery-less Industrial Internet of Things (IIoT) devices that are energized by energy harvesting (EH) must both finish latency-critical tasks and avoid capacitor brownouts simultaneously, but prior multi-agent learning approaches only optimize average performance, penalizing energy safety as a soft constraint and thus providing no assurances for energy-neutral operation. In this paper, a constrained, risk-sensitive multi-agent reinforcement learning framework is presented for joint task offloading and EH scheduling in battery-less 6G industrial networks. Learned dual variables that constrain brownout and energy-neutrality as hard budgets augment a centralized-training, decentralized-execution MAPPO backbone, a conditional-value-at-risk (CVaR) distributional critic is used to down-weight worst-case brownout, and a 6G ambient-backscatter transmission mode is used to enable communication in energy-scarce regimes. The method, L-MAPPO-EH, has the highest return and task completion, and reduces the brownout rate by a factor of three, on average, over the best learning baseline, and backscatter gains improve completion by an additional twenty-six percentage points under rare EH regimes, yielding a Pareto-non-dominated, statistically validated policy.