1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
Towards Adapting Reinforcement Learning Agents to New Tasks: Insights from Q-Values
Ashwin Ramaswamy, Ransalu Senanayake
While contemporary reinforcement learning research and applications have embraced policy gradient methods as the panacea of solving learning problems, value-based methods can still…
cs.RO2022
Uncertainty-Aware Online Merge Planning with Learned Driver Behavior
Liam A. Kruse, Esen Yel, Ransalu Senanayake +1
Safe and reliable autonomy solutions are a critical component of next-generation intelligent transportation systems. Autonomous vehicles in such systems must reason about complex a…
cs.LG2022
Renaissance Robot: Optimal Transport Policy Fusion for Learning Diverse Skills
Julia Tan, Ransalu Senanayake, Fabio Ramos
Deep reinforcement learning (RL) is a promising approach to solving complex robotics problems. However, the process of learning through trial-and-error interactions is often highly…