Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation

no code implementations • 7 Apr 2020 • Mahdi Kazemi Moghaddam, Qi Wu, Ehsan Abbasnejad, Javen Qinfeng Shi

Through empirical studies, we show that our agent, dubbed as the optimistic agent, has a more realistic estimate of the state value during a navigation episode which leads to a higher success rate.

Reinforcement Learning (RL) Visual Navigation

Learning for Visual Navigation by Imagining the Success

no code implementations • 28 Feb 2021 • Mahdi Kazemi Moghaddam, Ehsan Abbasnejad, Qi Wu, Javen Shi, Anton Van Den Hengel

ForeSIT is trained to imagine the recurrent latent representation of a future state that leads to success, e. g. either a sub-goal state that is important to reach before the target, or the goal state itself.

Navigate Reinforcement Learning (RL) +1