42 citations · 146 across the 13 of their papers we have counts for
Showing 2019Show all
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cs.LG2019★ 14 cited
Deep Bayesian Reward Learning from Preferences
Daniel S. Brown, Scott Niekum
Bayesian inverse reinforcement learning (IRL) methods are ideal for safe imitation learning, as they allow a learning agent to reason about reward uncertainty and the safety of a l…
cs.LG2019★ 30 cited
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…
cs.LG2019★ 21 cited
Risk-Aware Active Inverse Reinforcement Learning
Daniel S. Brown, Yuchen Cui, Scott Niekum
Active learning from demonstration allows a robot to query a human for specific types of input to achieve efficient learning. Existing work has explored a variety of active query s…