Anti-Martingale Proximal Policy Optimization.
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DOI: 10.1109/tcyb.2022.3170355
Abstract: Since the sample data after one exploration process can only be used to update network parameters once in on-policy deep reinforcement learning (DRL), a high sample efficiency is necessary to accelerate the training process of… read more here.
Keywords: policy optimization; martingale proximal; proximal policy; policy ... See more keywords