Search Results for author:
Found 4 papers, 2 papers with code
A Game of Bundle Adjustment -- Learning Efficient Convergence
Bundle adjustment is the common way to solve localization and mapping.
A Game of Bundle Adjustment - Learning Efficient Convergence
Bundle adjustment is the common way to solve localization and mapping.
Can Q-learning solve Multi Armed Bantids?
We claim that this stems from variance differences between policies, which causes two problems: The first problem is the "Boring Policy Trap" where each policy have a different implicit exploration depends on its rewards variance, and leaving a boring, or low variance, policy is less likely due to its low implicit exploration.
Adaptive Symmetric Reward Noising for Reinforcement Learning
We claim that some of the brittleness stems from variance differences, i. e. when different environment areas - states and/or actions - have different rewards variance.
