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Policy is a sequence of decision functions, where decision function is a mapping from state space to the decision space.
In the standard PbD formulation of this problem, a parametrised function is learned, mapping from state, x t, which denotes the current position of the demonstrator's hand to x ̇ t, the hand's displacement.
The second challenge is finding a procedure for learning a mapping from state activations to state posterior probabilities that provides the same excellent generalization to unseen conditions that has been found with sparse coding.
The second issue is that the mapping from state activations returned by some solver to state posterior probabilities is less straightforward than was implemented in [20] and [24] and in this paper.
A non-minimal inclusion function,, is a non-unique mapping from state space to data space and contains the smallest interval box that encloses the image g([ x]).
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At each time step, the agent implements a mapping from states to probabilities of selecting each possible action.
Goals are encoded as reward functions, expressing the desirability of each world state; the planner must find a policy (mapping from states to actions) that maximizes future rewards.
For \ \Gamma, \Delta \in \bG, \Gamma \bR \Delta\) is read "\ \Delta\) is accessible from \ \Gamma\)." A (propositional) valuation on a frame is a mapping, \ \bV\), that assigns to each propositional letter a mapping from states of the frame to truth values, true or false.
Usually, the covariance operator for a Hilbert space-valued process is an operator mapping from the state space into the state space and not into the dual, i.e., in the present situation mapping H − α ( D ) into itself.
A policy g : S → G represents a mapping method from state space to decision space.
Recall that a policy π is a mapping from each state s and action a to the probability π s, a) of taking action a when in state s.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com