71 citations · 141 across the 9 of their papers we have counts for
3 papers · 1 filter
Learning Latent State Spaces for Planning through Reward Prediction
Aaron Havens, Yi Ouyang, Prabhat Nagarajan +1
Model-based reinforcement learning methods typically learn models for high-dimensional state spaces by aiming to reconstruct and predict the original observations. However, drawing…
ChainerRL: A Deep Reinforcement Learning Library
Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka +1
In this paper, we introduce ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning framework. ChainerRL implements a c…
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan +1
A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator. This is because IRL typically seeks a rewa…