SOLVING ACTION SEMANTIC CONFLICT IN PHYSICALLY HETEROGENEOUS MULTI-AGENT REINFORCEMENT LEARNING WITH GENERALIZED ACTION-PREDICTION OPTIMIZATION

Solving Action Semantic Conflict in Physically Heterogeneous Multi-Agent Reinforcement Learning with Generalized Action-Prediction Optimization

Traditional multi-agent reinforcement learning (MARL) algorithms typically implement global parameter sharing across various Leather Long Wallet types of heterogeneous agents without meticulously differentiating between different action semantics.This approach results in the action semantic conflict problem, which decreases the generalization abili

read more