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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Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
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A major challenge in reinforcement learning is to determine which state-action pairs are responsible for future rewards that are delayed.
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Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems Anonymous Author(s) Affiliation Address email
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"equiconnected", which is a stronger result that we will define and introduce later in the paper.
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Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems
Neural Information Processing Systems
We present an application of the connectedness of these superlevel sets to the derivation of minimax theorems for robust reinforcement learning.
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