Learning in high dimensional continuous tasks is challenging, mainly when the
experience replay memory is very limited. We introduce a simple yet effective
experience sharing mechanism for deterministic policies in continuous action
domains for the future off-policy deep reinforcement learning applications in
which the allocated memory for the experience replay buffer is limited. To
overcome the extrapolation error induced by learning from other agents'
experiences, we facilitate our algorithm with a novel off-policy correction
technique without any action probability estimates. We test the effectiveness
of our method in challenging OpenAI Gym continuous control tasks and conclude
that it can achieve a safe experience sharing across multiple agents and
exhibits a robust performance when the replay memory is strictly limited.

本研究介绍了一种基于经验共享机制和无需动作概率估计的离策略校正技术，以应对在高维持续任务中，经验重现内存非常有限的挑战。该方法能够在具有严格限制的重现缓冲器内安全地共享多个代理人的经验，并在具有挑战性的 OpenAI Gym 连续控制任务中表现出坚韧的性能。