Design, evaluation and comparison of evolution and reinforcement learning models
- Authors: Mclean, Clinton Brett
- Date: 2002
- Subjects: Evolutionary computation Neural networks (Computer science) Reinforcement learning
- Language: English
- Type: Thesis , Masters , MEcon
- Identifier: vital:4625 , http://hdl.handle.net/10962/d1006493
- Description: This work presents the design, evaluation and comparison of evolution and reinforcement learning models, in isolation and combined in Darwinian and Lamarckian frameworks, with a particular emphasis being placed on their adaptive nature in response to environments that become increasingly unstable. Our ultimate objective is to determine whether hybrid models of evolution and learning can demonstrate adaptive qualities beyond those of such models when applied in isolation. This work demonstrates the limitations of evolution, reinforcement learning and Lamarckian models in dealing with increasingly unstable environments, while noting the effective adaptive nature of a Darwinian model to assimilate increasing levels of instability. This is shown to be a result of the Darwinian evolution model's ability to separate learning at two levels, the population's experience of the environment over the course of many generations and the individual's experience of the environment over the course of its lifetime. Thus, knowledge relating to the general characteristics of the environment over many generations can be maintained in the population's genotypes with phenotype (reinforcement) learning being utilized to adapt a particular agent to the particular characteristics of its environment. Lamarckian evolution, though, is shown to demonstrate adaptive characteristics that are highly effective in response to the stable environments. Selection and reproduction combined with reinforcement learning creates a model that has the ability to utilize useful knowledge produced by reinforcements, as opposed to random mutations, to accelerate the search process. As a result the influence of individual learning on the populations evolution is shown to be more successful when applied in the more direct Lamarckian form. Based on our results demonstrating the success of Lamarckian strategies in stable environments and Darwinian strategies in unstable environments, hybrid Darwinian/Lamarckian models are created with a view towards combining the advantages of both forms of evolution to produce a superior adaptive capability. Our investigation demonstrates that such hybrid models can effectively combine the adaptive advantageous of both Darwinian and Lamarckian evolution to provide a more effective capability of adapting to a range of conditions, from stable to unstable, appropriately adjusting the required degree of inheritance in response to the requirements of the environment.
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Implementing non-photorealistic rendering enhancements with real-time performance
- Authors: Winnemöller, Holger
- Date: 2002 , 2013-05-09
- Subjects: Computer animation , Computer graphics , Real-time data processing
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:4580 , http://hdl.handle.net/10962/d1003135 , Computer animation , Computer graphics , Real-time data processing
- Description: We describe quality and performance enhancements, which work in real-time, to all well-known Non-photorealistic (NPR) rendering styles for use in an interactive context. These include Comic rendering, Sketch rendering, Hatching and Painterly rendering, but we also attempt and justify a widening of the established definition of what is considered NPR. In the individual Chapters, we identify typical stylistic elements of the different NPR styles. We list problems that need to be solved in order to implement the various renderers. Standard solutions available in the literature are introduced and in all cases extended and optimised. In particular, we extend the lighting model of the comic renderer to include a specular component and introduce multiple inter-related but independent geometric approximations which greatly improve rendering performance. We implement two completely different solutions to random perturbation sketching, solve temporal coherence issues for coal sketching and find an unexpected use for 3D textures to implement hatch-shading. Textured brushes of painterly rendering are extended by properties such as stroke-direction and texture, motion, paint capacity, opacity and emission, making them more flexible and versatile. Brushes are also provided with a minimal amount of intelligence, so that they can help in maximising screen coverage of brushes. We furthermore devise a completely new NPR style, which we call super-realistic and show how sample images can be tweened in real-time to produce an image-based six degree-of-freedom renderer performing at roughly 450 frames per second. Performance values for our other renderers all lie between 10 and over 400 frames per second on homePC hardware, justifying our real-time claim. A large number of sample screen-shots, illustrations and animations demonstrate the visual fidelity of our rendered images. In essence, we successfully achieve our attempted goals of increasing the creative, expressive and communicative potential of individual NPR styles, increasing performance of most of them, adding original and interesting visual qualities, and exploring new techniques or existing ones in novel ways. , KMBT_363 , Adobe Acrobat 9.54 Paper Capture Plug-in
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