4 citations · 6 across the 7 of their papers we have counts for
4 papers · 1 filter
ConTraIRL: Factorized Contrastive Abstractions for Transferable IRL
Yikang Gui, Bikramjit Banerjee, Prashant Doshi
Reward transfer in Inverse Reinforcement Learning (IRL) is unreliable when policies must generalize to unseen combinations of environment dynamics and task goals. We propose Factor…
Inversely Learning Transferable Rewards via Abstracted States
Yikang Gui, Prashant Doshi
Inverse reinforcement learning (IRL) has progressed significantly toward accurately learning the underlying rewards in both discrete and continuous domains from behavior data. The…
A Novel Variational Lower Bound for Inverse Reinforcement Learning
Yikang Gui, Prashant Doshi
Inverse reinforcement learning (IRL) seeks to learn the reward function from expert trajectories, to understand the task for imitation or collaboration thereby removing the need fo…
IRL with Partial Observations using the Principle of Uncertain Maximum Entropy
Kenneth Bogert, Yikang Gui, Prashant Doshi
The principle of maximum entropy is a broadly applicable technique for computing a distribution with the least amount of information possible while constrained to match empirically…