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Learning in high dimensional continuous tasks is challenging, mainly when theexperience replay memory is very limited. We introduce a simple yet effectiveexperience sharing mechanism for deterministic policies in continuous actiondomains for the future off-policy deep reinforcement learning applications inwhich the allocated memory for the experience replay buffer is limited. Toovercome the extrapolation error induced by learning from other agents'experiences, we facilitate our algorithm with a novel off-policy correctiontechnique without any action probability estimates. We test the effectivenessof our method in challenging OpenAI Gym continuous control tasks and concludethat it can achieve a safe experience sharing across multiple agents andexhibits a robust performance when the replay memory is strictly limited.

Safe and Robust Experience Sharing for Deterministic Policy Gradient Algorithms