linear
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For SALE: State-Action Representation Learning for Deep Reinforcement Learning
We extensively study the design space of these embeddings and highlight important design considerations.
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- North America > United States > Massachusetts > Hampshire County > Amherst (0.04)
- North America > Puerto Rico (0.04)
- Asia > Middle East > Jordan (0.04)
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- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
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Accelerated On-Device Forward Neural Network Training with Module-Wise Descending Asynchronism
However, FGD's dependencies across layers hinder parallel computation and can lead to inefficient resource utilization.
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- Europe > Italy > Calabria > Catanzaro Province > Catanzaro (0.04)
- Asia > Middle East > UAE (0.04)
- Asia > China > Jiangsu Province > Nanjing (0.04)
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- Research Report > Promising Solution (0.46)
- Research Report > New Finding (0.46)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
Recovering from Out-of-sample States via Inverse Dynamics in Offline Reinforcement Learning
However, such pessimism for out-of-sample data could be too restricted and sample inefficient, as not all out-of-sample(unseen) states are not generalizable [20].
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- Europe > Switzerland > Zürich > Zürich (0.14)
- North America > United States > Louisiana > Orleans Parish > New Orleans (0.04)
- North America > United States > California > Santa Clara County > Mountain View (0.04)
- (7 more...)
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- South America > Chile > Santiago Metropolitan Region > Santiago Province > Santiago (0.05)
- North America > United States > California > Alameda County > Livermore (0.04)
Industry: Government > Regional Government > North America Government > United States Government (0.46)
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APPENDIX APreprocessingandtokenizationdetails
When inserting a new trial, the oldest trial will be removed. We use a population size of 25 and tournament size of 5.
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