Search
Skip to Search Results- 93Reinforcement Learning
- 19Machine Learning
- 10Artificial Intelligence
- 6Transfer Learning
- 5Planning
- 5Representation Learning
- 83Graduate and Postdoctoral Studies (GPS), Faculty of
- 83Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 5Computing Science, Department of
- 5Computing Science, Department of/Technical Reports (Computing Science)
- 3WISEST Summer Research Program
- 3WISEST Summer Research Program/WISEST Research Posters
-
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...
-
Design and Optimal Operation of a Virtual Power Plant with Bidirectional Electric Vehicle Chargers
DownloadSpring 2023
Virtual power plants (VPPs) can enhance reliability and efficiency of power systems with a high share of renewables. However, their adoption largely depends on their profitability, which is difficult to maximize due to the heterogeneity of their components, different sources of uncertainty and...
-
2008
Lizotte, Daniel, Wang, Tao, Bowling, Michael, Schuurmans, Dale
Technical report TR08-16. We propose a dual approach to dynamic programming and reinforcement learning based on maintaining an explicit representation of visit distributions as opposed to value functions. An advantage of working in the dual is that it allows one to exploit techniques for...
-
2006
Wang, Tao, Schuurmans, Dale, Bowling, Michael
Technical report TR06-26. We investigate the dual approach to dynamic programming and reinforcement learning, based on maintaining an explicit representation of stationary distributions as opposed to value functions. A significant advantage of the dual approach is that it allows one to exploit...
-
Spring 2010
In this thesis, a Reinforcement Learning (RL) method called Sarsa is used to dynamically tune a PI-controller for a Continuous Stirred Tank Heater (CSTH) experimental setup. The proposed approach uses an approximate model to train the RL agent in the simulation environment before implementation...
-
Spring 2019
In the reinforcement learning (RL) problem an agent must learn how to act optimally through trial-and-error interactions with a complex, unknown, stochastic environment. The actions taken by the agent influence not just the immediate reward it observes but also the future states and rewards it...
-
Spring 2011
Off-policy reinforcement learning is useful in many contexts. Maei, Sutton, Szepesvari, and others, have recently introduced a new class of algorithms, the most advanced of which is GQ(lambda), for off-policy reinforcement learning. These algorithms are the first stable methods for general...