Learning to Explore via Meta-Policy Gradient

ICML 2018 Tianbing XuQiang LiuLiang ZhaoJian Peng

The performance of off-policy learning, including deep Q-learning and deep deterministic policy gradient (DDPG), critically depends on the choice of the exploration policy. Existing exploration methods are mostly based on adding noise to the on-going actor policy and can only explore local regions close to what the actor policy dictates... (read more)

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