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What is the return-to-go in reinforcement learning?
In reinforcement learning, the return is defined as some function of the rewards. For example, you can have the discounted return, where you multiply the rewards received at later time steps by ...
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What is the difference between the definition of a stationary policy in reinforcement learning and contextual bandit?
A stationary policy is a function that maps a state to a probability distribution of actions.
In a contextual bandit problem, a state itself does not include the history. But in a reinforcement ...
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