5 citations · 8 across the 3 of their papers we have counts for
5 papers
Critic Guided Segmentation of Rewarding Objects in First-Person Views
Andrew Melnik, Augustin Harter, Christian Limberg +3
This work discusses a learning approach to mask rewarding objects in images using sparse reward signals from an imitation learning dataset. For that, we train an Hourglass network…
Towards robust and domain agnostic reinforcement learning competitions
William Hebgen Guss, Stephanie Milani, Nicholay Topin +26
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field. Desp…
Solving Physics Puzzles by Reasoning about Paths
Augustin Harter, Andrew Melnik, Gaurav Kumar +3
We propose a new deep learning model for goal-driven tasks that require intuitive physical reasoning and intervention in the scene to achieve a desired end goal. Its modular struct…
Modularization of End-to-End Learning: Case Study in Arcade Games
Andrew Melnik, Sascha Fleer, Malte Schilling +1
Complex environments and tasks pose a difficult problem for holistic end-to-end learning approaches. Decomposition of an environment into interacting controllable and non-controlla…
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong +26
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle c…