Sentence examples for sum of rewards from inspiring English sources

The value of a policy is the sum of rewards obtained from execution of actions described in the policy.

Furthermore, the rewards can often also be decomposed as a sum of rewards related to individual variables.

Under these assumptions we can easily identify a luck-neutralizing distribution under the assumption of a constant sum of rewards in the following four-person case: This distribution neutralizes luck (not necessarily uniquely: there may be other luck-neutralizing distributions).

For the purpose of learning, we define the objective function for user as the discounted sum of rewards in each spectrum access period with discount factor, that is, (18).

The Q Learning approach adopted is unique to our work in the news reports modelling application as the approach provides more weight-age to rewards from moves taken when the search space is less localized and distant from the goal while calculating a discounted sum of rewards present in the trust path to goal.

More precisely, given the proposed definition of reward and given that animals discount future rewards (Chung & Herrnstein, 1967), any behavioral policy, π, that maximizes the sum of discounted rewards (S D R ) also minimizes the sum of discounted deviations from the setpoint, and vice versa.

The equivalency of reward maximization and physiological stability objectives in our model (Equation 5) shows that optimizing either homeostasis or sum of discounted rewards corresponds to prescribing a principle of least action applied to the surprise function.

For example we added the below text after equation 14: The equivalency of reward maximization and physiological stability objectives in our model (equation 5) shows that optimizing either homeostasis or sum of discounted rewards corresponds to prescribing a principle of least action applied to the surprise function.

The definition of future reward used here is very similar to that found in the field of reinforcement learning, in which a key goal is to predict "the sum of future rewards" [2] [4].

The best fuzzy inference rules are obtained by using the quality function Q π (s,a) that is defined as the expected sum of discounted rewards from the initial state s0 under policy π as follows: Q π ( s, a ) = E π [ ∑ t = 0 ∞ γ t r ( s t, a t ) s 0 = s, a 0 = a ].

The quality function Q π (s,a) is defined as the expected sum of discounted rewards from the initial state s 0 under the optimal action policy π as follows: {Q_{pi} }(s,a) = {mathbb{E}_{pi} }left[sumlimits_{t = 0}^{infty} {{theta^{t}}r({s_{t}},{a_{t}})} left| {{s_{0}} = s,{a_{0}} = a} right.right].