The Role of Exploration for Task Transfer in Reinforcement Learning

no code implementations • 11 Oct 2022 • Jonathan C Balloch, Julia Kim, and Jessica L Inman, Mark O Riedl

The exploration--exploitation trade-off in reinforcement learning (RL) is a well-known and much-studied problem that balances greedy action selection with novel experience, and the study of exploration methods is usually only considered in the context of learning the optimal policy for a single learning task.

reinforcement-learning Reinforcement Learning (RL) +1

Unbiasing Semantic Segmentation For Robot Perception using Synthetic Data Feature Transfer

no code implementations • 11 Sep 2018 • Jonathan C Balloch, Varun Agrawal, Irfan Essa, Sonia Chernova

We show that pretraining real-time segmentation architectures with synthetic segmentation data instead of ImageNet improves fine-tuning performance by reducing the bias learned in pretraining and closing the \textit{transfer gap} as a result.

Image Segmentation Segmentation +1