Wszystkie pola: incremental - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Epoch-incremental reinforcement learning algorithms
Autorzy:: Zajdel, R.
Powiązania:: https://bibliotekanauki.pl/articles/330530.pdf
Data publikacji:: 2013
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: reinforcement learning
epoch incremental algorithm
grid world
uczenie ze wzmocnieniem
algorytm przyrostowy
Opis:: In this article, a new class of the epoch-incremental reinforcement learning algorithm is proposed. In the incremental mode, the fundamental TD(0) or TD(λ) algorithm is performed and an environment model is created. In the epoch mode, on the basis of the environment model, the distances of past-active states to the terminal state are computed. These distances and the reinforcement terminal state signal are used to improve the agent policy.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2013, 23, 3; 623-635
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Robust observer design for Sugeno systems with incremental quadratic nonlinearity in the consequent
Autorzy:: Moodi, H.
Farrokhi, M.
Powiązania:: https://bibliotekanauki.pl/articles/331108.pdf
Data publikacji:: 2013
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: nonlinear Sugeno model
incremental quadratic constraint
robust observer
model Sugeno nieliniowy
obserwator odporny
Opis:: This paper is concerned with observer design for nonlinear systems that are modeled by T–S fuzzy systems containing parametric and nonparametric uncertainties. Unlike most Sugeno models, the proposed method contains nonlinear functions in the consequent part of the fuzzy IF-THEN rules. This will allow modeling a wider class of systems with smaller modelling errors. The consequent part of each rule contains a linear part plus a nonlinear term, which has an incremental quadratic constraint. This constraint relaxes the conservativeness introduced by other regular constraints for nonlinearities such as the Lipschitz conditions. To further reduce the conservativeness, a nonlinear injection term is added to the observer dynamics. Simulation examples show the effectiveness of the proposed method compared with the existing techniques reported in well-established journals.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2013, 23, 4; 711-723
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Multiquery motion planning in uncertain spaces: Incremental adaptive randomized roadmaps
Autorzy:: Khaksar, Weria
Uddin, Md Zia
Torresen, Jim
Powiązania:: https://bibliotekanauki.pl/articles/329773.pdf
Data publikacji:: 2019
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: motion planning
roadmap
sampling based planner
obstacle avoidance
planowanie ruchu
mapa drogowa
unikanie przeszkód
Opis:: Sampling-based motion planning is a powerful tool in solving the motion planning problem for a variety of different robotic platforms. As its application domains grow, more complicated planning problems arise that challenge the functionality of these planners. One of the main challenges in the implementation of a sampling-based planner is its weak performance when reacting to uncertainty in robot motion, obstacles motion, and sensing noise. In this paper, a multi-query sampling-based planner is presented based on the optimal probabilistic roadmaps algorithm that employs a hybrid sample classification and graph adjustment strategy to handle diverse types of planning uncertainty such as sensing noise, unknown static and dynamic obstacles and an inaccurate environment map in a discrete-time system. The proposed method starts by storing the collision-free generated samples in a matrix-grid structure. Using the resulting grid structure makes it computationally cheap to search and find samples in a specific region. As soon as the robot senses an obstacle during the execution of the initial plan, the occupied grid cells are detected, relevant samples are selected, and in-collision vertices are removed within the vision range of the robot. Furthermore, a second layer of nodes connected to the current direct neighbors are checked against collision, which gives the planner more time to react to uncertainty before getting too close to an obstacle. The simulation results for problems with various sources of uncertainty show a significant improvement compared with similar algorithms in terms of the failure rate, the processing time and the minimum distance from obstacles. The planner is also successfully implemented and tested on a TurtleBot in four different scenarios with uncertainty.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2019, 29, 4; 641-654
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: D* Extra Lite: A dynamic A* with search-tree cutting and frontier-gap repairing
Autorzy:: Przybylski, M.
Putz, B.
Powiązania:: https://bibliotekanauki.pl/articles/329769.pdf
Data publikacji:: 2017
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: shortest path planning
incremental heuristic search
mobile robot navigation
video game
planowanie najkrótszej ścieżki
wyszukiwanie heurystyczne
nawigacja robota mobilnego
gra wideo
Opis:: Searching for the shortest-path in an unknown or changeable environment is a common problem in robotics and video games, in which agents need to update maps and to perform re-planning in order to complete their missions. D* Lite is a popular incremental heuristic search algorithm (i.e., it utilizes knowledge from previous searches). Its efficiency lies in the fact that it re-expands only those parts of the search-space that are relevant to registered changes and the current state of the agent. In this paper, we propose a new D* Extra Lite algorithm that is close to a regular A*, with reinitialization of the affected search-space achieved by search-tree branch cutting. The provided worst-case complexity analysis strongly suggests that D* Extra Lite’s method of reinitialization is faster than the focused approach to reinitialization used in D* Lite. In comprehensive tests on a large number of typical two-dimensional path-planning problems, D* Extra Lite was 1.08 to 1.94 times faster than the optimized version of D* Lite. Moreover, while demonstrating that it can be particularly suitable for difficult, dynamic problems, as the problem-complexity increased, D* Extra Lite’s performance further surpassed that of D*Lite. The source code of the algorithm is available on the open-source basis.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2017, 27, 2; 273-290
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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