3 citations · 4 across the 4 of their papers we have counts for
4 papers · 1 filter
CrystalGym: A New Benchmark for Materials Discovery Using Reinforcement Learning
Prashant Govindarajan, Mathieu Reymond, Antoine Clavaud +3
In silico design and optimization of new materials primarily relies on high-accuracy atomic simulators that perform density functional theory (DFT) calculations. While recent works…
Efficient Morphology-Aware Policy Transfer to New Embodiments
Michael Przystupa, Hongyao Tang, Martin Jagersand +4
Morphology-aware policy learning is a means of enhancing policy sample efficiency by aggregating data from multiple agents. These types of policies have previously been shown to he…
MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning
Rafael Rafailov, Kyle Hatch, Victor Kolev +3
We study the problem of offline pre-training and online fine-tuning for reinforcement learning from high-dimensional observations in the context of realistic robot tasks. Recent of…
Searching for High-Value Molecules Using Reinforcement Learning and Transformers
Raj Ghugare, Santiago Miret, Adriana Hugessen +2
Reinforcement learning (RL) over text representations can be effective for finding high-value policies that can search over graphs. However, RL requires careful structuring of the…