Exploration in Deep Reinforcement Learning: A Comprehensive Survey. (arXiv:2109.06668v3 [cs.AI] UPDATED)

Web: http://arxiv.org/abs/2109.06668

Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Learning
(MARL) have achieved significant success across a wide range of domains, such
as game AI, autonomous vehicles, robotics and finance. However, DRL and deep
MARL agents are widely known to be sample-inefficient and millions of
interactions are usually needed even for relatively simple game settings, thus
preventing the wide application in real-industry scenarios. One bottleneck
challenge behind is the well-known exploration problem, i.e., how to
efficiently explore the unknown environments and …

ai arxiv deep exploration learning reinforcement learning survey