Reinforcement learning (RL) is currently a popular research topic in control engineering . DESSCA is a kernel density estimation-based state-space coverage acceleration(DESSCA) is proposed, which improves the ES concept by prioritizinginfrequently visited states for a more balanced coverage of the state spaceduring training . DesSCA has been shown to be a simple yet effective algorithmicextension to the established ES approach . The study was published in the journal Computer Science, published on October 4, 2013. For confidential support call the Samaritans on 08457 90 90 90 or visit a local Samaritans branch, see www.samaritans.org for details. In the U.S. call the National Suicide Prevention Lifeline on suicide prevention.

Author(s) : Maximilian Schenke, Oliver Wallscheid

Links : PDF - Abstract

Code :
Coursera

Keywords : coverage - samaritans - state - dessca - estimation -

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