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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Provably Robust Temporal Difference Learning for Heavy-Tailed Rewards
Neural Information Processing Systems
We corroborate our theoretical results with numerical experiments.
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2c3ddf4bf13852db711dd1901fb517fa-AuthorFeedback.pdf
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As[R1]38 has pointed out, our novel interpretation of KL term gives new insights and variations on online Bayesian learning.39 Since UCL samples the weight parameters only once for each iteration, applying it to actor-critic based42 reinforcement learning algorithm becomes possible.
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