提出协同采样方法，提升多智能体强化学习的收敛可靠性。

Independent on-policy policy gradient algorithms are widely used for multi-agent reinforcement learning (MARL) in cooperative and no-conflict games, but they are known to converge sub-optimally when each agent's individual policy gradient points away from an optimal joint equilibrium. Going beyond prior work, we observe that sub-optimal convergence can still arise even when the expected individual policy gradients of each agent point toward the optimal joint solution. After collecting a finite set of trajectories, stochasticity in independent action sampling can cause the joint data distribution to deviate from the expected joint on-policy distribution. This \textit{sampling error} w.r.t. the joint on-policy distribution produces inaccurate gradient estimates that can make agents converge sub-optimally. We hypothesize that joint sampling error can be reduced through coordinated action selection and that doing so will increase the reliability of policy gradient learning in MARL (i.e., the probability of converging to an optimal joint policy). To test this hypothesis, we first introduce an adaptive action sampling approach to reduce joint sampling error in the Centralized Training with Decentralized Execution setting. Our method, Cooperative Sampling Error Reduction (CoSER), continually adapts a centralized behavior policy to place higher probability on joint actions that are under-sampled w.r.t. the current joint policy. We then empirically evaluate CoSER on a diverse set of multi-agent games and demonstrate that (1) CoSER reduces joint sampling error more efficiently than independent on-policy sampling and (2) this reduction increases the reliability of independent policy gradient algorithms.