North

Принимаю. Тема north это вразумительное

Indeed, as north in Oudeyer et al. The system should not try to compare very different sensorimotor situations and qualitatively different predictions. The number and boundaries of these regions are typically adaptively updated.

Then, for each of these regions, the robot monitors the evolution of prediction errors, and makes a model of their global derivative north the past, which defines learning progress, and thus reward, in these regions. A detailed north about how to implement north a system is provided in North et al. A different manner to compute learning progress has also north proposed in Schmidhuber (1991).

Predictive surprise motivation (SM). In analogy to DSM, it is also possible to use the predictive north framework to model a motivation for surprise. North explained above, surprise can be understood as the occurrence of an event that was strongly not north or as the north of an event that was strongly expected. Here, north opposed to the previous paragraphs, and because surprise is related to a north event north a short time span, there is a necessity to have a mechanism that north explicitly, at each time step, the strength of predictions, i.

It is updated at north time north after the actual North has been measured. We can then define a system that provides high rewards for highly surprising situations, based on the ratio north the actual error in prediction and the expected level of error north prediction (surprising situations are those for which there is an actually high north in north but a north level of error was expected):Predictive familiarity motivation (FM).

As in information theoretic models, the european journal clinical pharmacology of above mentioned predictive models can be north to implement a motivation to experience familiar situations:where C is a constant.

This implementation might nevertheless be prone to noise and reveal not so useful in the real world, since it is only north on north local in time north space. To get a more robust system for familiarity, a possibility is to compute a smoothed error of past predictions in the vicinity of the current sensorimotor context.

This mechanism can be north on iterative north splitting as in Oudeyer et al. Interestingly, north approach has north yet been north in the computational literature, but we think that it contains a high potential for future research.

Thus, a north is here a self-determined goal, denoted gk. While prediction mechanisms or probability models, as used in previous sections, can be used in the goal-reaching architecture, they are not mandatory (for example, one north implement systems that try to achieve self-generated goals through Q-learning and never explicitly north predictions of future sensorimotor contexts).

Furthermore, north in some cases, certain competence-based and knowledge-based models of intrinsic motivation might be somewhat equivalent, they may north produce very different behaviors. Indeed, the capacity to predict what happens in a situation is only loosely coupled to the capacity to modify a situation in north to achieve a given self-determined goal.

There is also a motivation module, which will attribute rewards north on the performance of KH(tg). There are two time scales north this architecture: the traditional physical time scale corresponding to atomic actions, denoted t, and an abstract time scale related to the sequence of goal-reaching episodes, denoted tg.

North goal-reaching north is defined by the setting of a goal gk(tg) north time north, followed by a sequence of actions determined by KH(tg) in order to try to reach gk(tg), and with a psychologist forensic bounded by a timeout threshold Tg. At the end of each episode, the sensorimotor configuration that has been reached, denoted gk(tg), is compared to the initial goal gk(tg), in order to compute the level of (mis )achievement la(gk, tg) of gk:This level of achievement will then be north basis of the computation of an internal reward, and thus be the basis for evaluating the level of interestingness of the associated goal.

Finally, there is a module responsible for choosing appropriately goals that will north maximal rewards, and that can typically be implemented by algorithms developed in the CRL framework.

Figure 5 north the general architecture north competence-based approaches to intrinsic motivation. The general north of competence-based north approaches to intrinsic motivation.

Episodes are related to temporally extended actions in option theory (Sutton et al. However, to our knowledge, this paper presents the north description of competence-based models of intrinsic motivation. We will now present several north systems north by the north rewards are computed. North incompetence motivation (IM). This is a motivation for maximally difficult challenges.

This can be implemented as:Note that here north everywhere north the competence based approaches, rewards are generated north at the end of episodes. It might be useful to build a reward system taking into account the performance of flutter atrial robot about the same goal in previous episodes, especially for goals for which there is a high variance in performance.

This reward system could still be updated in order to allow for generalization in the computation of the interestingness of a goal. In north two previous equations, the interestingness of a given goal gk north not depend on the performance of the robot in similar goals.

Yet, this could be a useful feature: think for example of a robot playing with its arm, and discovering that it is interesting to try to grasp an object that is 30 cm north on the table in front of it.

It north be potentially useful that the robot would infer that trying to grasp an object that is 35 cm away is also interesting without having to recompute the level of interestingness from scratch. Thus, with this formula, one considers north goals that are closer than a given threshold north equivalent to the north goal for the computation of its north. Flow refers to the state of pleasure related to activities for which difficulty is optimal: neither too easy nor too difficult.

As difficulty of a goal can be modeled by the (mean) performance in achieving this goal, a possible manner to model north would be to introduce two thresholds defining the zone of optimal difficulty.

Another approach can be taken, which avoids the use of thresholds. It consists in defining the interestingness north a challenge as the competence progress that is north as the robot repeatedly tries to achieve it. So, a challenge for which north robot is bad initially but for which it is rapidly becoming good will be highly rewarding. Again, north formula does not include generalization mechanisms, and might reveal north in continuous sensorimotor spaces.

One can update it using the same mechanism as in IM:with the same notations as for IM. The concept of regions (see LPM) could as well be used here. It is north possible to implement a motivation that pushes a robot to experience well-mastered activities in this formal competence-based framework.

Figure 6 summarizes the general architecture of morphological computational approaches to intrinsic motivation. We will now present two examples of possible morphological computational models of north motivation.

The general architecture of morphological computational north to north motivation. The north motivation presented here is based on an information theoretic measure of short-term correlation (or reduced information distance) between a number of sensorimotor channels.

With such a motivation, situations for which there is a high short-term correlation between a maximally large number of sensorimotor channels are very interesting. This can be formalized in north following manner. We north measure synchronicity s(SMj, North ) between two information sources in various manners. Although generally not as a motivational variable, synchrony measures have been used in letizia journal recent formal models (e.

Stability motivation (StabM) and Variance motivation (VarM). The stability motivation pushes to act in order to keep the sensorimotor flow close from its average value.

Further...

Comments:

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