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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Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning
Neural Information Processing Systems
Humans are great observers who can learn by aggregating external knowledge from various sources, including observations from others' policies of attempting a task.
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7b647a7d88f4d6319bf0d600d168dbeb-Supplemental.pdf
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It is therefore difficult to discuss and understand what choices, among the high-levelalgorithmic options as well as low-levelimplementation details, matter.
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7b647a7d88f4d6319bf0d600d168dbeb-Paper.pdf
Neural Information Processing Systems
It is therefore difficult to discuss and understand what choices, among the high-levelalgorithmic options as well as low-levelimplementation details, matter.
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