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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dd03f856fc7f2efeec8b1c796284561d-Paper-Conference.pdf
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Short-horizon tasks require few steps: e.g., go to a tree and chop it down, dig a hole.
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Iterative Reachability Estimation for Safe Reinforcement Learning
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We theoretically establish that our algorithms almost surely converge to locally optimal policies of our safe optimization framework.
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Provably (More) Sample-Efficient Offline RL with Options
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Planning in long-horizon tasks is challenging in reinforcement learning (RL) (Co-Reyes et al., 2018;
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Provably (More) Sample-Efficient Offline RL with Options
Neural Information Processing Systems
Planning in long-horizon tasks is challenging in reinforcement learning (RL) (Co-Reyes et al., 2018;
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