Table 4.

Conceptual comparison of OMTOS with mainstream multi-agent DRL algorithms.

Method	Learning Type	Strength	Limitation in 6G IoV Offloading
MADDPG	Off-policy actor–critic with centralized critic and decentralized actors	Suitable for continuous multi-agent control and coordinated decision-making	Can be sensitive to hyperparameters and may face scalability and stability issues as the number of vehicle agents increases
MAPPO	On-policy multi-agent policy optimization	Stable policy updates and effective cooperative learning	Requires fresh samples for policy updates, which can increase training cost in dynamic vehicular environments
Conventional SAC	Off-policy entropy-regularized actor–critic	Good exploration and sample reuse	Does not explicitly model multi-agent coordination across multiple vehicular agents
Proposed OMTOS	Multi-agent SAC-based off-policy learning	Supports entropy-based exploration, replay-buffer reuse, and adaptive multi-tier offloading	Requires future empirical comparison with additional multi-agent DRL baselines under identical settings