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- 1Policy Evaluation
- 1Reinforcement Learning
- 1Reinforcement Learning, General Value Functions, Off-policy Learning, Robots
- 1Reinforcement learning
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Fall 2018
Knowledge is central to intelligence. Intelligence can be thought of as the ability to acquire knowledge and apply it effectively. Despite being a subject of intense interest in artificial intelligence, it is not yet clear what the best approach is for an intelligent system to acquire and...
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Fall 2018
In this thesis, we make two contributions in learning with artificial neural networks. Artificial neural networks have made great success in various challenging domains. Our first contribution is a new technique named cross-propagation that does cross-validation online. In cross-validation,...
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Fall 2011
We present a new family of gradient temporal-difference (TD) learning methods with function approximation whose complexity, both in terms of memory and per-time-step computation, scales linearly with the number of learning parameters. TD methods are powerful prediction techniques, and with...
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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...