Abstract:To solve the low learning efficiency and slow convergence due to the complex relationship among intelligent agents in multi-agent reinforcement learning, this study proposes a two-level attention mechanism based on MADDPG-Attention. The mechanism adds soft and hard attention mechanisms to the Critic network of the MADDPG algorithm and learns the learnable experience among intelligent agents through the attention mechanism to improve the mutual learning efficiency of the agents. Since the single-level soft attention mechanism assigns learning weights to completely irrelevant intelligent agents, hard attention is employed to determine the necessity of learning between two intelligent agents, and the agents with irrelevant information are cut. Then soft attention is adopted to determine the importance of learning between two intelligent agents, and the learning weights are assigned according to the importance distribution to learn from the agents with available experience. Meanwhile, tests on a collaborative navigation environment with multi-agent particles show that the MADDPG-Attention algorithm has a clearer understanding of complex relationships and achieves a success rate of more than 90% in all three environments, which improves the learning efficiency and accelerates the convergence rate.