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Skip to Search Results- 22Reinforcement learning
- 3Machine learning
- 2Non-player character
- 2Robotics
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- 2Sarsa
- 1Bastani, Meysam
- 1Cutumisu, Maria
- 1Gendron-Bellemare, Marc
- 1Gomez Noriega, Diego Fernando
- 1Hao, Yongchang
- 1Jeya Veeraiah, Vivek Veeriah
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Fall 2009
Character behaviours in computer role-playing games have a significant impact on game-play, but are often difficult for story authors to implement and modify. Many computer games use custom scripts to control the behaviours of non-player characters (NPCs). Therefore, a story author must write...
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Fall 2015
The control of powered prosthetic arms has been researched for over 50 years, yet prosthetic control remains an open problem, not just from a research perspective, but from a clinical perspective as well. Significant advances have been made in the manufacture of highly functional prosthetic...
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Towards Practical Offline Reinforcement Learning: Sample Efficient Policy Selection and Evaluation
DownloadSpring 2024
Offline reinforcement learning (RL) involves learning policies from datasets, rather than online interaction. The dissertation first investigates a critical component in offline RL: offline policy selection (OPS). Given that most offline RL algorithms require careful hyperparameter tuning, we...
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Fall 2017
The idea of an amputee playing the piano with all the flair and grace of an able-handed person may seem like a futuristic fantasy. While many prosthetic limbs look lifelike, finding one that also moves naturally has proved more of a challenge for both researchers and amputees. Even though...
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Spring 2012
We study linear estimation based on perturbed data when performance is measured by a matrix norm of the expected residual error, in particular, the case in which there are many unknowns, but the “best” estimator is sparse, or has small L1-norm. We propose a Lasso-like procedure that finds the...
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Spring 2024
In reinforcement learning, the notion of state plays a central role. A reinforcement learning agent requires the state to evaluate its current situation, select actions, and construct a model of the environment. In the classic setting, it is assumed that the environment provides the agent with...
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Fall 2009
Learning and planning are two fundamental problems in artificial intelligence. The learning problem can be tackled by reinforcement learning methods, such as temporal-difference learning, which update a value function from real experience, and use function approximation to generalise across...
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Spring 2024
The ability to learn good representations of states is essential for solving large reinforcement learning problems, where exploration, generalization, and transfer are particularly challenging. The Laplacian representation is a promising approach to address these problems by inducing intrinsic...
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Spring 2014
Each patient with Type-1 diabetes must decide how much insulin to inject before each meal to maintain an acceptable level of blood glucose. The actual injection dose is based on a formula that takes current blood glucose level and the meal size into consideration. While following this insulin...
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Spring 2016
In model-based reinforcement learning a model is learned which is then used to find good actions. What model to learn? We investigate these questions in the context of two different approaches to model-based reinforcement learning. We also investigate how one should learn and plan when the reward...