4 papers
Beyond Success Rates: Trainability and Extractability for Offline GCRL
Jan Malte Töpperwien, Aditya Mohan, Marius Lindauer
Offline goal-conditioned reinforcement learning (GCRL) is typically benchmarked by the best tuned success rate of each method. This score measures attainable performance, but it do…
EquiBim: Learning Symmetry-Equivariant Policy for Bimanual Manipulation
Zhiyuan Zhang, Aditya Mohan, Seungho Han +3
Robotic imitation learning has achieved impressive success in learning complex manipulation behaviors from demonstrations. However, many existing robot learning methods do not expl…
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
Jannis Becktepe, Julian Dierkes, Carolin Benjamins +7
Hyperparameters are a critical factor in reliably training well-performing reinforcement learning (RL) agents. Unfortunately, developing and evaluating automated approaches for tun…
Moments Matter:Stabilizing Policy Optimization using Return Distributions
Dennis Jabs, Aditya Mohan, Marius Lindauer
Deep Reinforcement Learning (RL) agents often learn policies that achieve the same episodic return yet behave very differently, due to a combination of environmental (random transi…