2k citations · 2.2k across the 27 of their papers we have counts for
28 papers
Models Matter: The Impact of Single-Step Retrosynthesis on Synthesis Planning
Paula Torren-Peraire, Alan Kai Hassen, Samuel Genheden +4
Retrosynthesis consists of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found with the go…
Two-Memory Reinforcement Learning
Zhao Yang, Thomas. M. Moerland, Mike Preuss +1
While deep reinforcement learning has shown important empirical success, it tends to learn relatively slow due to slow propagation of rewards information and slow update of paramet…
First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation
Zhao Yang, Thomas M. Moerland, Mike Preuss +1
Go-Explore achieved breakthrough performance on challenging reinforcement learning (RL) tasks with sparse rewards. The key insight of Go-Explore was that successful exploration req…
Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction
Alan Kai Hassen, Paula Torren-Peraire, Samuel Genheden +3
Retrosynthesis is the task of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found. Consequ…
On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfer
Marco Pleines, Konstantin Ramthun, Yannik Wegener +12
Autonomously trained agents that are supposed to play video games reasonably well rely either on fast simulation speeds or heavy parallelization across thousands of machines runnin…
Generalization, Mayhems and Limits in Recurrent Proximal Policy Optimization
Marco Pleines, Matthias Pallasch, Frank Zimmer +1
At first sight it may seem straightforward to use recurrent layers in Deep Reinforcement Learning algorithms to enable agents to make use of memory in the setting of partially obse…