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Skip to Search Results- 83Reinforcement Learning
- 17Machine Learning
- 8Artificial Intelligence
- 6Transfer Learning
- 5Planning
- 5Representation Learning
- 1Abbasi Brujeni, Lena
- 1Abbasi-Yadkori, Yasin
- 1Aghakasiri, Kiarash
- 1Alikhasi, Mahdi
- 1Asadi Atui, Kavosh
- 1Banafsheh Rafiee
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Spring 2022
The world offers unprecedented amounts of data in real-world domains, from which we can develop successful decision-making systems. It is possible for reinforcement learning (RL) to learn control policies offline from such data but challenging to deploy an agent during learning in safety-critical...
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Fall 2019
Q-learning can be difficult to use in continuous action spaces, because a difficult optimization has to be solved to find the maximal action. Some common strategies have been to discretize the action space, solve the maximization with a powerful optimizer at each step, restrict the functional...
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Spring 2021
Learning about many things can provide numerous benefits to a reinforcement learning system. For example, learning many auxiliary value functions, in addition to optimizing the environmental reward, appears to improve both exploration and representation learning. The question we tackle in this...
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Spring 2015
Much of the focus on finding good representations in reinforcement learning has been on learning complex non-linear predictors of value. Methods like policy gradient, that do not learn a value function and instead directly represent policy, often need fewer parameters to learn good policies....
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Spring 2024
In model-based reinforcement learning, an agent can improve its policy by planning: learning from experience generated by a model. Search control is the problem of determining which starting state should be used to generate this experience. Given a limited planning budget, an agent should be...
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Spring 2023
Reinforcement learning (RL) defines a general computational problem where the learner must learn to make good decisions through interactive experience. To be effective in solving this problem, the learner must be able to explore the environment, make accurate predictions about the future, and...
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Fall 2022
In this thesis, we investigate the empirical performance of several experience replay techniques. Efficient experience replay plays an important role in model-free reinforcement learning by improving sample efficiency through reusing past experience. However, replay-based methods were largely...
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Fall 2023
With the increasing complexity and capacity of modern Field-Programmable Gate Arrays (FPGAs), there is a growing demand for efficient FPGA computer-aided design (CAD) tools, particularly at the placement stage. While some previous works, such as RLPlace, have explored the efficacy of single-state...
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Spring 2022
Policy gradient (PG) estimators are ineffective in dealing with softmax policies that are sub-optimally saturated, which refers to the situation when the policy concentrates its probability mass on sub-optimal actions. Sub-optimal policy saturation may arise from a bad policy initialization or a...
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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...