Traditional multi-agent reinforcement learning (MARL) algorithms typically implement global parameter sharing across various types of heterogeneous agents without meticulously differentiating between different action semantics. This approach results in the action semantic conflict problem. which decreases the generalization ability of policy networks across heterogeneous types of agen... https://foldlyers.shop/product-category/spanners/