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Multi-Task Reinforcement Learning with Context-based Representations
[article]
2021
arXiv
pre-print
The benefit of multi-task learning over single-task learning relies on the ability to use relations across tasks to improve performance on any single task. While sharing representations is an important mechanism to share information across tasks, its success depends on how well the structure underlying the tasks is captured. In some real-world situations, we have access to metadata, or additional information about a task, that may not provide any new insight in the context of a single task
arXiv:2102.06177v2
fatcat:gmwlp2lwavhi7itxkok7dj6tdy