Performance Dynamics and Termination Errors in Reinforcement Learning: A Unifying Perspective

11 Feb 2019Nikki Lijing KuangClement H. C. Leung

In reinforcement learning, a decision needs to be made at some point as to whether it is worthwhile to carry on with the learning process or to terminate it. In many such situations, stochastic elements are often present which govern the occurrence of rewards, with the sequential occurrences of positive rewards randomly interleaved with negative rewards... (read more)

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