Exploration in multi-agent reinforcement learning is a challenging problem, especially in environments with sparse rewards. We propose a general method for efficient exploration by sharing experience amongst agents. Our proposed algorithm, called Shared Experience Actor-Critic (SEAC), applies experience sharing in an actor-critic framework. We evaluate SEAC in a collection of sparse-reward multi-agent environments and find that it consistently outperforms two baselines and two state-of-the-art algorithms by learning in fewer steps and converging to higher returns. In some harder environments, experience sharing makes the difference between learning to solve the task and not learning at all.

中文翻译：

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多智能体强化学习的共享经验演员-评论家

探索多智能体强化学习是一个具有挑战性的问题，尤其是在奖励稀疏的环境中。我们提出了一种通过在代理之间共享经验来进行有效探索的通用方法。我们提出的算法称为共享体验演员-评论家 (SEAC)，在演员-评论家框架中应用经验共享。我们在一组稀疏奖励的多智能体环境中评估 SEAC，发现它通过以更少的步骤学习并收敛到更高的回报，始终优于两个基线和两个最先进的算法。在一些更困难的环境中，经验分享使学习解决任务和根本不学习之间存在差异。