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.
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
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Empirically, the low-variance baseline of POMO makes RL training fast and stable, and it is more resistant to local minima compared to previous approaches.
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Improving Online Rent-or-Buy Algorithms with Sequential Decision Making and ML Predictions
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In this work we study online rent-buy problems as a sequential decision making problem.
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Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
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A deep reinforcement learning (DRL) agent observes its states through observations, which may contain natural measurement errors or adversarial noises.
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