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Skip to Search Results- 7Deep Reinforcement Learning
- 4Reinforcement Learning
- 2Transfer Learning
- 1Action Advising
- 1Advice Collection
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Fall 2023
In reinforcement learning (RL), agents learn to maximize a reward signal using nothing but observations from the environment as input to their decision making processes. Whether the agent is simple, consisting of only a policy that maps observations to actions, or complex, containing auxiliary...
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Spring 2023
Vision is one of the essential means for humans to perceive the world. Similarly, today's intelligent robot agents rely on camera images to perform complex tasks in the real world. Due to the ever-changing nature of the real world, intelligent robot agents must continually learn from...
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Feature Generalization in Deep Reinforcement Learning: An Investigation into Representation Properties
DownloadFall 2022
In this thesis, we investigate the connection between the properties and the generalization performance of representations learned by deep reinforcement learning algorithms. Much of the earlier work on representation learning for reinforcement learning focused on designing fixed-basis...
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Highway Lane change under uncertainty with Deep Reinforcement Learning based motion planner
DownloadSpring 2020
Motion Planning is a fundamental component of a mobile robot to reach its goal safely avoiding collision. For a self-driving car on a highway, the presence of non-communicating vehicles, specially those whose intent is unknown, creates a lot of uncertainty for the motion planner in generating a...
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Spring 2022
Reinforcement learning (RL) has shown great success in solving many challenging tasks via the use of deep neural networks. Although the use of deep learning for RL brings immense representational power to the arsenal, it also causes sample inefficiency. This means that the algorithms are...
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Fall 2022
The rise of Deep Learning (DL) and its assistance in learning complex feature representations significantly impacted Reinforcement Learning (RL). Deep Reinforcement Learning (DRL) made it possible to apply RL to complex real-world problems and even achieve human-level performance. One of these...
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Spring 2020
Single-agent optimization tasks, also referred to as single-player games, include any domain with an agent whose goal is to maximize an objective function(s), without interference from any other agents. Such tasks have been studied for decades. For example, in 2006, NASA automated the design of...