Information-Directed Exploration for Deep Reinforcement Learning
ICLR, Volume abs/1812.07544, 2019.
Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is therefore heteroscedastic. Classical exploration strategies such as upper confidence bound algorithms and Thompson sampling fail to appropriately account for he...More
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