The phrase "action probabilities" is correct and usable in written English.
It can be used in contexts related to decision-making, statistics, or machine learning, where it refers to the likelihood of various actions occurring.
Example: "In reinforcement learning, the agent evaluates the action probabilities to determine the best course of action based on its current state."
Alternatives: "likelihood of actions" or "probabilities of actions".
The Q values are associated with policy of actions and thus justifying the use of provenance path probability values in determining action probabilities.
The sequence of actions (A = {a_1, a_2,ldots,a_T}) during this episode represents the SMILES generated and the product of the action probabilities (P(A) = prod _{t = 1}^T{pi (a_t mid s_t)}) represents the model likelihood of the sequence formed.
Note that, to make the notation simple, we will drop the superscript x from the these parameters, but, whenever we refer to these action probabilities, it is understood implicitly that they are specific to each TF species.
q(t) is the action probability distribution of the primary user at instant t, and p i (t) is the action probability distribution of the i th secondary user at instant t.
end{aligned}}} (17) Table 2 BS1 actions and corresponding probabilities in three-sectored cellular network Case number BS1 action Probability Case 1 Egoistic (bar {psi }) Case 2 Altruistic for MS2 (1/2)ψ Case 3 Altruistic for MS3 (1/2)ψ.
Neural Networks, 22(3), 220 228] of such selectionist style of learning reveals a resemblance between its ensemble-level dynamics governing the change of action probability and Bayesian learning where evidence (in this case, reward) is distributively applied to all action alternatives.
Naturally emerging are the notion of secondary (conditioned) reinforcement predicting the average reward associated with a stimulus, and the notion of actor critic architecture involving concurrent learning of both action probability and reward prediction.
This function may be interpreted as the mass action probability of neurons firing if their membrane potential is over the threshold, and can be derived from a stochastic neuron models [6, 36].
The action probability distributions when velocity is excluded (right-hand distribution) and included (left-hand distribution, i.e., shaded bars) are compared clearly showing the importance of each feature.
Thus, these two variables, personal involvement and action probability, may have often been confounded.
Thus, being personally involved is not necessary to develop the illusion, and a high action probability is not necessary either.
