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- 19Reinforcement learning
- 3Machine learning
- 2Artificial Intelligence
- 2Non-player character
- 2Reinforcement Learning
- 1Al-Saffar, M.
- 1Bastani, Meysam
- 1Bowling, Michael
- 1Cutumisu, Maria
- 1Edwards, Ann L
- 1Gendron-Bellemare, Marc
- 15Graduate Studies and Research, Faculty of
- 15Graduate Studies and Research, Faculty of/Theses and Dissertations
- 3Computing Science, Department of
- 3Computing Science, Department of/Technical Reports (Computing Science)
- 1Electrical and Computer Engineering, Department of
- 1Electrical and Computer Engineering, Department of/Journal Articles (Electrical and Computer Engineering)
In this thesis we propose a computational model of animal behavior in spatial navigation, based on reinforcement learning ideas. In the field of computer science and specifically artificial intelligence, replay refers to retrieving and reprocessing the experiences that are stored in an abstract...
Adaptive and Autonomous Switching: Shared Control of Powered Prosthetic Arms Using Reinforcement LearningDownload
Powered prosthetic arms with numerous controllable functions (i.e., grip patterns or movable joints) can be challenging to operate. Gated control---a common control method for myoelectric arms and other human-machine interfaces---allows users to select a function by switching through a static...
Reinforcement Learning is a formalism for learning by trial and error. Unfortunately, trial and error can take a long time to find a solution if the agent does not efficiently explore the behaviours available to it. Moreover, how an agent ought to explore depends on the task that the agent is...
Most story-based games today have manually-scripted non-player characters (NPCs) and the scripts are usually simple and repetitive since it is time-consuming for game developers to script each character individually. ScriptEase, a publicly-available author-oriented developer tool, attempts to...
Facial expressions and other body language are important for human commu- nication. They complement speech and make the process of communication simple and sustainable. However, the process of communication using existing approaches to human-machine interaction is not intuitive as that of human...
Understanding how an artificial agent may represent, acquire, update, and use large amounts of knowledge has long been an important research challenge in artificial intelligence. The quantity of knowledge, or knowing a lot, may be nicely thought of as making and updat- ing many predictions about...
This thesis presents new algorithms for dealing with large scale reinforcement learning problems. Central to this work is the Atari 2600 platform, which acts as both a rich evaluation framework and a source of challenges for existing reinforcement learning methods. Three contributions are...
Technical report TR07-12. One key topic in reinforcement learning is function approximation which is critical for the success of reinforcement learning in domains with large state spaces. Unfortunately, function approximation can lead to several problems including the suboptimality of the...
Non-Player Character (NPC) behaviors in today’s computer games are mostly generated from manually written scripts. The high cost of manually creating complex behaviors for each NPC to exhibit intelligence in response to every situation in the game results in NPCs with repetitive and artificial...
Model-free off-policy temporal-difference (TD) algorithms form a powerful component of scalable predictive knowledge representation due to their ability to learn numerous counter- factual predictions in a computationally scalable manner. In this dissertation, we address and overcome two...