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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Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees Dohyeong Kim
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However, the nonlinearity of risk measures makes it challenging to achieve convergence and optimality.
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LLMs' flexibility is largely due to an "in-context learning" effect that emerges when
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