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 Reinforcement Learning




89b9e0a6f6d1505fe13dea0f18a2dcfa-Paper.pdf

Neural Information Processing Systems

Weevaluate PI-SAC agents by comparing against uncompressed PI-SAC agents, other compressed and uncompressed agents, and SAC agents directly trained from pixels.



OnlineDecisionBasedVisualTrackingvia ReinforcementLearning

Neural Information Processing Systems

A deep visual tracker is typically based on either object detection or template matching while each of them is only suitable for a particular group of scenes. It is straightforward to consider fusing them together to pursue more reliable tracking. However, this is not wise as they follow different tracking principles.


884d247c6f65a96a7da4d1105d584ddd-Paper.pdf

Neural Information Processing Systems

DDPG [24]extends Q-learning to continuous control based on the Deterministic Policy Gradient [31] algorithm, which learns a deterministic policyฯ€(s;ฯ†) parameterized byฯ†to maximize the Q-function to approximate themaxoperator.