Adaptive Edge Task Offloading via Parameterized Multi-Objective Reinforcement Learning With Hybrid Action Space

In 6G networks, Multi-access Edge Computing (MEC) enables ultra-low latency and high reliability for Internet of Things (IoT) applications. However, optimizing resource allocation in MEC is challenging due to dynamic network conditions and limited computational resources. To address these challenges, this study proposes a Hybrid Multi-Objective Soft Actor-Critic (HMO-SAC) algorithm, which integrates Multi-Objective Reinforcement Learning (MORL) within a hybrid action space. The method dynamically balances multiple optimization objectives, leveraging a hybrid action space to make decisions involving both discrete and continuous parameters, such as task offloading targets and resource allocation. Additionally, an Improved Near-on Experience Replay (INER) mechanism is introduced to mitigate extrapolation errors in off-policy sampled data. Simulation results demonstrate that HMO-SAC improves convergence speed by 14% on average and reduces the task completion time and energy consumption by 23% compared to state-of-the-art methods.