Sentence examples for standard reinforcement from inspiring English sources

The modular sizes of members, standard reinforcement bar diameters, spacing requirements of reinforcing bars, architectural requirements and other practical requirements in addition to relevant provisions are considered to obtain directly constructible designs without any further modifications.

The method is compared, in terms of simulation results and other aspects, with other standard reinforcement learning methods.

The preliminary results presented in this paper demonstrate that a standard reinforcement learning approach is capable of learning a control signal selection policy to prevent the network state distribution from straying far from a specified target distribution.

For a standard reinforcement learning procedure, all of these game sequences (s_t) were considered as a Markov decision process directly (MDP), where (s_t=x_1,ldots_2,ldots,a_{t-1},x_t).

As most previous studies were based on the standard reinforcement learning theories, the neural substrate of uncertainty is unclear, and even worse, it is still elusive whether animals and humans consider uncertainty for action selection.

The theory of standard reinforcement learning (Sutton & Barto, 1998), which mainly focuses on the reward expectation in the striatum and the reward prediction error in midbrain dopamine neurons, can predict the reward-estimation-based action selection of animals and humans (for review, see Daw & Doya, 2006; Corrado & Doya, 2007; O'Doherty et al., 2007; Dayan & Niv, 2008).

Also assessed are the effects of the standard support and reinforcement.

During feedback, support officers will discuss findings of the review, identify where the implementation of policies and practices links with setting continuous quality improvement processes and standards, provide reinforcement when positive progress has been made, discuss identified deficits and facilitate reflection, problem solving, goal setting and action planning.

As early as the mid-1970s, studies had demonstrated that they could be trained through operant conditioning using standard schedules of reinforcement.

A standard critique in reinforcement is that gene flow might overwhelm the selective advantage of being choosy [45].

Recent studies employ a model-based strategy and show that the strategy or a hybrid model of model-free and model-based strategy offers better prediction of human behaviors than only a standard model-free reinforcement learning model (Hampton et al., 2006; Dayan & Niv, 2008; Glascher et al., 2010).