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Skip to Search Results- 19Reinforcement learning
- 15Robotics
- 3Machine learning
- 2Learning
- 2Non-player character
- 2Role-playing game
- 2Parker, Christopher A. C.
- 1Bastani, Meysam
- 1Cutumisu, Maria
- 1Gendron-Bellemare, Marc
- 1Hajebi, Kiana
- 1Hao, Yongchang
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Spring 2012
Mirian HosseinAbadi, MahdiehSadat
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...
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Spring 2023
Choosing an appropriate action representation is an integral part of solving robotic manipulation problems. Published approaches include latent action models, which train context-conditioned neural networks to map lowdimensional latent actions to high-dimensional actuation commands. Such models...
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Spring 2023
Wheelchair-mounted robotic manipulators have the potential to help the elderly and individuals living with disabilities carry out their activities of daily living independently. While robotics researchers focus on assistive tasks from the perspective of various control schemes and motion types, ...
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Spring 2020
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...
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Fall 2009
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...
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Fall 2021
An oft-ignored challenge of real-world reinforcement learning is that, unlike standard simulated environments, the real world does not pause when agents make learning updates. As standard simulated environments do not address this real-time aspect of learning, most available implementations of...
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Spring 2018
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...
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Collective decision-making in decentralized multiple-robot systems: a biologically inspired approach to making up all of your minds
DownloadFall 2009
Decision-making is an important operation for any autonomous system. Robots in particular must observe their environment and compute appropriate responses. For solitary robots and centralized multiple-robot systems, decision-making is a relatively straightforward operation, since only a single...
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Decision Frequency Adaptation in Reinforcement Learning Using Continuous Options with Open-Loop Policies
DownloadFall 2023
In classic reinforcement learning(RL) for continuous control, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter. Setting it too short may increase the problem’s difficulty by requiring the agent to make numerous decisions...
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Fall 2015
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...