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Skip to Search Results- 1Augmented Lagrangian optimization
- 1Game theory
- 1Graph Laplacian
- 1Hyperparameter robustness
- 1Neural Network Sparsity
- 1Poker
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Spring 2024
In machine learning, sparse neural networks provide higher computational efficiency and in some cases, can perform just as well as fully-connected networks. In the online and incremental reinforcement learning (RL) problem, Prediction Adapted Networks (Martin and Modayil, 2021) is an algorithm...
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Spring 2024
The ability to learn good representations of states is essential for solving large reinforcement learning problems, where exploration, generalization, and transfer are particularly challenging. The Laplacian representation is a promising approach to address these problems by inducing intrinsic...
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Fall 2009
One way to create a champion level poker agent is to compute a Nash Equilibrium in an abstract version of the poker game. The resulting strategy is then used to play in the full game. With this approach, translation is required between the full and abstract games in order to use the...