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Attentive Experience Replay
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Experience replay (ER) has become an important component of deep reinforcement learning (RL) algorithms. ER enables RL algorithms to reuse past experiences for the update of current policy. By reusing a previous state for training, the RL agent would learn more accurate value estimation and better decision on that state. However, as the policy is continually updated, some states in past experiences become rarely visited, and optimization over these states might not improve the overall
doi:10.1609/aaai.v34i04.6049
fatcat:ivpircajqveftcpi3bb4qomtt4