4 citations · 15 across the 7 of their papers we have counts for
3 papers · 1 filter
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague +10
This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery. Since RL is fairly data intensive, training agents `on-the-fly' by…
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson +1
Multi-agent reinforcement learning typically suffers from the problem of sample inefficiency, where learning suitable policies involves the use of many data samples. Learning from…
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte +1
Conventional supervised learning methods typically assume i.i.d samples and are found to be sensitive to out-of-distribution (OOD) data. We propose Generative Causal Representation…