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In this paper, a novel Multi-agent Reinforcement Learning (MARL) approach,Multi-Agent Continuous Dynamic Policy Gradient (MACDPP) was proposed to tacklethe issues of limited capability and sample efficiency in various scenarioscontrolled by multiple agents. It alleviates the inconsistency of multipleagents' policy updates by introducing the relative entropy regularization tothe Centralized Training with Decentralized Execution (CTDE) framework with theActor-Critic (AC) structure. Evaluated by multi-agent cooperation andcompetition tasks and traditional control tasks including OpenAI benchmarks androbot arm manipulation, MACDPP demonstrates significant superiority in learningcapability and sample efficiency compared with both related multi-agent andwidely implemented signal-agent baselines and therefore expands the potentialof MARL in effectively learning challenging control scenarios.

Effective Multi-Agent Deep Reinforcement Learning Control with Relative Entropy Regularization