16 citations · 43 across the 33 of their papers we have counts for
10 papers · 1 filter
PODNet: A Neural Network for Discovery of Plannable Options
Ritwik Bera, Vinicius G. Goecks, Gregory M. Gremillion +2
Learning from demonstration has been widely studied in machine learning but becomes challenging when the demonstrated trajectories are unstructured and follow different objectives.…
A Narration-based Reward Shaping Approach using Grounded Natural Language Commands
Nicholas Waytowich, Sean L. Barton, Vernon Lawhern +1
While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniq…
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern +2
This paper investigates how to efficiently transition and update policies, trained initially with demonstrations, using off-policy actor-critic reinforcement learning. It is well-k…
Learning from Observations Using a Single Video Demonstration and Human Feedback
Sunil Gandhi, Tim Oates, Tinoosh Mohsenin +1
In this paper, we present a method for learning from video demonstrations by using human feedback to construct a mapping between the standard representation of the agent and the vi…
On Memory Mechanism in Multi-Agent Reinforcement Learning
Yilun Zhou, Derrik E. Asher, Nicholas R. Waytowich +1
Multi-agent reinforcement learning (MARL) extends (single-agent) reinforcement learning (RL) by introducing additional agents and (potentially) partial observability of the environ…
Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation
David Watkins-Valls, Jingxi Xu, Nicholas Waytowich +1
We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments. Our framework takes a pre-built 3D scan of a real e…