no code implementations • ICLR 2019 • Vikas Dhiman, Shurjo Banerjee, Jeffrey M. Siskind, Jason J. Corso
Multi-goal reinforcement learning (MGRL) addresses tasks where the desired goal state can change for every trial.
no code implementations • CVPR 2023 • Jacob Krantz, Shurjo Banerjee, Wang Zhu, Jason Corso, Peter Anderson, Stefan Lee, Jesse Thomason
We present Iterative Vision-and-Language Navigation (IVLN), a paradigm for evaluating language-guided agents navigating in a persistent environment over time.
no code implementations • 23 Oct 2020 • Shurjo Banerjee, Jesse Thomason, Jason J. Corso
In each trial, the pair first cooperates to localize the robot on a global map visible to the Commander, then the Driver follows Commander instructions to move the robot to a sequence of target objects.
no code implementations • 2 Oct 2019 • Salimeh Yasaei Sekeh, Madan Ravi Ganesh, Shurjo Banerjee, Jason J. Corso, Alfred O. Hero
In this work, firstly, we assert that OSFS's main assumption of having data from all the samples available at runtime is unrealistic and introduce a new setting where features and samples are streamed concurrently called OSFS with Streaming Samples (OSFS-SS).
no code implementations • 25 Sep 2018 • Vikas Dhiman, Shurjo Banerjee, Jeffrey M. Siskind, Jason J. Corso
We do this by adapting the Floyd-Warshall algorithm for RL and call the adaptation Floyd-Warshall RL (FWRL).
1 code implementation • 7 Feb 2018 • Vikas Dhiman, Shurjo Banerjee, Brent Griffin, Jeffrey M. Siskind, Jason J. Corso
However, when trained and tested on different sets of maps, the algorithm fails to transfer the ability to gather and exploit map-information to unseen maps.
no code implementations • ICLR 2018 • Shurjo Banerjee, Vikas Dhiman, Brent Griffin, Jason J. Corso
As the title of the paper by Mirowski et al. (2016) suggests, one might assume that DRL-based algorithms are able to “learn to navigate” and are thus ready to replace classical mapping and path-planning algorithms, at least in simulated environments.