4 papers
Hybrid Classical/RL Local Planner for Ground Robot Navigation
Vishnu D. Sharma, Jeongran Lee, Matthew Andrews +1
Local planning is an optimization process within a mobile robot navigation stack that searches for the best velocity vector, given the robot and environment state. Depending on how…
SACPlanner: Real-World Collision Avoidance with a Soft Actor Critic Local Planner and Polar State Representations
Khaled Nakhleh, Minahil Raza, Mack Tang +6
We study the training performance of ROS local planners based on Reinforcement Learning (RL), and the trajectories they produce on real-world robots. We show that recent enhancemen…
Learning Algorithms for Regenerative Stopping Problems with Applications to Shipping Consolidation in Logistics
Kishor Jothimurugan, Matthew Andrews, Jeongran Lee +1
We study regenerative stopping problems in which the system starts anew whenever the controller decides to stop and the long-term average cost is to be minimized. Traditional model…
Evolution of Q Values for Deep Q Learning in Stable Baselines
Matthew Andrews, Cemil Dibek, Karina Palyutina
We investigate the evolution of the Q values for the implementation of Deep Q Learning (DQL) in the Stable Baselines library. Stable Baselines incorporates the latest Reinforcement…