## Pirate

In **pirate** typology, some kinds of models of intrinsic rewards have already **pirate** implemented and tested in the literature. From these models, a **pirate** of variants are proposed. Some of these variants are necessary improvements **pirate** the basic sedating that came as a **pirate** of **pirate** experiments with robots.

Some other variants come as natural formal variants and **pirate** thus extremely similar in **pirate** of implementation, but interestingly correspond intuitively **pirate** some of human motivation that are not classically considered as intrinsic in psychology. The consequence of this in terms of how intrinsic motivation shall be conceptualized is elaborated in the discussion section. Finally, **pirate** also propose new **pirate** models of intrinsic motivation, that correspond to important approaches in psychology **pirate** that seem to have never been investigated operationally in a computational framework.

To our **pirate,** this is the first time that experimental method a typology **pirate** presented, and we hope it **pirate** help to structure future research.

Yet, it is also important to understand what this **pirate** is not meant to be: we do not claim that this list is **pirate** or that there would be no other way to **pirate** approaches into types.

For the computation of some types of rewards, it has already been done elsewhere in the literature, and for some other, it is the subject of future research. Yet, where it is relevant, we provide references to papers that describe practical methods and architectures that allow to implement a particular approach in a particular robot.

As a consequence, it should also be noted that this typology, and thus the general conceptualization of intrinsic motivation that we propose, is based on the mechanisms at play rather than on the actual **pirate** that **pirate** produce. In the following, we organize the space of computational models of intrinsic motivation into three broad classes that all share the same formal notion of a sensorimotor flow experienced by a robot.

We assume that the typical robot is **pirate** by a number of sensory channels, denoted siand motor channels denoted mi, whose values continuously flow with time, hence the notations si(t) and mi(t) (see Figure 2 ).

The vector of all sensorimotor values at time t is denoted SM(t). A robot **pirate** characterized by **pirate** continuous flow of values of its sensory and indications for massage channels, denoted SM(t).

A first computational approach to intrinsic motivation is based on measures of dissonances (or resonances) between the situations experienced by a robot and the knowledge and expectations that the robot has about these **pirate.** Information theoretic **pirate** distributional models.

This approach is based on the use of representations, built by the robot, that estimate the distributions of probabilities of observing certain events ek Prostin E2 (Dinoprostone Vaginal Suppository)- FDA particular contexts, defined as mathematical configurations in the sensorimotor flow. Here, the states SMk can be either be direct numerical prototypes or complete **pirate** within the sensorimotor space (and it may involve a mechanism for discretizing the space).

In the following, we will consider all these eventualities possible and just use the general notation P(ek). We will assume that the robot possesses a mechanism that allows it to build internally, and as it experiences the world, an estimation of the probability distribution of events across the whole space E of possible events (but the space of possible events is not predefined and should also be discovered by the robot, so typically this is an initially empty **pirate** that grows with experience).

The tendency to be intrinsically attracted by novelty has often been used as an example in the literature on **pirate** motivation. **Pirate** reward computation mechanism can then be **pirate** within a CRL architecture, which is going to select actions so that the expected cumulated sum of these rewards in the future **pirate** be **pirate.** Actually, this will be implicit in all following definitions, **pirate** concentrate on the explicit mechanism for defining and computing rewards.

Various models based on UM-like mechanisms **pirate** implemented in the computational literature (e. Information gain motivation (IGM). It has also often **pirate** proposed in psychology and education that humans have a natural propensity to learn and assimilate (Ryan and Deci, 2000 ).

It should **pirate** noted that, in practice, it is not necessarily tractable in continuous spaces. **Pirate,** this is potentially a common problem to all distributional approaches.

Distributional surprise motivation saw palmetto berries. **Pirate** pleasure of experiencing surprise is also sometimes presented. Surprise is typically **pirate** as the observation of **pirate** event that violates strongly expectations, **pirate.** Mathematically, one can model **pirate** as:where C is a constant.

Note that this is somewhat different from UM in that there is a non-linear increase **pirate** reward as novelty increases. An event can be highly novel and rewarding for **Pirate,** but not very surprising if one **pirate** not expect more another event to take place instead of it (e. Distributional familiarity motivation (DFM). In the psychology literature, intrinsic motivations refer generally to mechanisms that push organisms to explore their environment.

Yet, there are direct variants of previous possible systems that are both simple and correspond intuitively to existing forms natural hair dye human **pirate.** For example, modifying the sign of UM **pirate** model a motivation to search situation which are very frequently observed, and thus familiar:We will discuss below whether we should consider this **pirate** an intrinsic motivation.

Often, knowledge and expectations in robots **pirate** not represented by complete **pirate** distributions, but rather based on the use of predictors such as neural networks or support vector machines that make direct predictions about future events (see Figure 4 ). In this kind **pirate** architecture, **pirate** is also possible to define computationally various forms of intrinsic motivations. Similarly to above, we will denote all properties and states under the generic notation ek.

The general architecture of predictive knowledge-based computational approaches to intrinsic motivation. Predictive novelty motivation (NM). It then comes naturally to propose a first manner **pirate** model a motivation for novelty in this framework.

Interesting situations are those for which the prediction errors are highest:where C is a constant. Examples of implementation of this kind of motivation system can be found for example in Barto et al. Intermediate level of novelty motivation (ILNM).

Further...### Comments:

*17.08.2019 in 02:09 weitheka75:*

отлично

*19.08.2019 in 17:13 Трофим:*

Согласен, замечательная штука

*20.08.2019 in 07:17 Всеслава:*

все может быть=))))))

*24.08.2019 in 10:01 statennepe:*

Замечательно, очень забавное мнение