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Raksha Kumaraswamy

2 accepted papers

2021

Continual Auxiliary Task Learning

NeurIPS 2021poster

Learning auxiliary tasks, such as multiple predictions about the world, can provide many benefits to reinforcement learning systems. A variety of off-policy learning algorithms have been developed to learn such predictions, but as yet there is little work on how to adapt the behavior to gather usefu…

Cited by 10Source ↗PDF ↗Scholar ↗
2018

Context-dependent upper-confidence bounds for directed exploration

NeurIPS 2018poster

Directed exploration strategies for reinforcement learning are critical for learning an optimal policy in a minimal number of interactions with the environment. Many algorithms use optimism to direct exploration, either through visitation estimates or upper confidence bounds, as opposed to data-inef…

Cited by 21Source ↗PDF ↗Scholar ↗