71 citations · 160 across the 17 of their papers we have counts for
9 papers · 1 filter
Accelerating Policy Gradient by Estimating Value Function from Prior Computation in Deep Reinforcement Learning
Md Masudur Rahman, Yexiang Xue
This paper investigates the use of prior computation to estimate the value function to improve sample efficiency in on-policy policy gradient methods in reinforcement learning. Our…
Group SELFIES: A Robust Fragment-Based Molecular String Representation
Austin Cheng, Andy Cai, Santiago Miret +3
We introduce Group SELFIES, a molecular string representation that leverages group tokens to represent functional groups or entire substructures while maintaining chemical robustne…
AnyMorph: Learning Transferable Polices By Inferring Agent Morphology
Brandon Trabucco, Mariano Phielipp, Glen Berseth
The prototypical approach to reinforcement learning involves training policies tailored to a particular agent from scratch for every new morphology. Recent work aims to eliminate t…
Offline Policy Comparison with Confidence: Benchmarks and Baselines
Anurag Koul, Mariano Phielipp, Alan Fern
Decision makers often wish to use offline historical data to compare sequential-action policies at various world states. Importantly, computational tools should produce confidence…
Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design
Kourosh Hakhamaneshi, Marcel Nassar, Mariano Phielipp +2
Being able to predict the performance of circuits without running expensive simulations is a desired capability that can catalyze automated design. In this paper, we present a supe…
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning
Hassam Sheikh, Kizza Frisbee, Mariano Phielipp
Application of ensemble of neural networks is becoming an imminent tool for advancing the state-of-the-art in deep reinforcement learning algorithms. However, training these large…
Instance based Generalization in Reinforcement Learning
Martin Bertran, Natalia Martinez, Mariano Phielipp +1
Agents trained via deep reinforcement learning (RL) routinely fail to generalize to unseen environments, even when these share the same underlying dynamics as the training levels.…
Hierarchical Policy Learning is Sensitive to Goal Space Design
Zach Dwiel, Madhavun Candadai, Mariano Phielipp +1
Hierarchy in reinforcement learning agents allows for control at multiple time scales yielding improved sample efficiency, the ability to deal with long time horizons and transfera…
Goal-conditioned Imitation Learning
Yiming Ding, Carlos Florensa, Mariano Phielipp +1
Designing rewards for Reinforcement Learning (RL) is challenging because it needs to convey the desired task, be efficient to optimize, and be easy to compute. The latter is partic…