9 citations · 10 across the 3 of their papers we have counts for
3 papers
cs.LG2020★ 1 cited
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation
Kei Ota, Devesh K. Jha, Diego Romeres +7
Humans quickly solve tasks in novel systems with complex dynamics, without requiring much interaction. While deep reinforcement learning algorithms have achieved tremendous success…
cs.LG2019★ 9 cited
Learning from Trajectories via Subgoal Discovery
Sujoy Paul, Jeroen van Baar, Amit K. Roy-Chowdhury
Learning to solve complex goal-oriented tasks with sparse terminal-only rewards often requires an enormous number of samples. In such cases, using a set of expert trajectories coul…
cs.LG2018
Trajectory-based Learning for Ball-in-Maze Games
Sujoy Paul, Jeroen van Baar
Deep Reinforcement Learning has shown tremendous success in solving several games and tasks in robotics. However, unlike humans, it generally requires a lot of training instances.…