Reinforcement Learning
"Reinforcement learning is learning what to do – how to map situations to actions – so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them."
– Sutton, Richard S. and Andrew G. Barto. Reinforcement Learning: An Introduction. (1.1). MIT Press, Cambridge, MA, 1998.
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GoalReductionwithLoop-RemovalAcceleratesRL andModels HumanBrainActivityinGoal-Directed Learning
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The pressure for survival prohibits slow, linear adaptation to different goals, i.e., learning value functions from scratch for each new objective. A quick and versatile paradigm is necessary forsuchgoal-directed learning scenarios.
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07d5938693cc3903b261e1a3844590ed-Supplemental.pdf
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Prior works havepresented several means to combat this phenomenon in IL.
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07d5938693cc3903b261e1a3844590ed-Paper.pdf
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