research
Reward Uncertainty Moved Into Q-Space
QVIRL learns a variational distribution over optimal Q-values and recovers a posterior over rewards, including from raw pixels.
Summary
QVIRL learns a variational distribution over optimal Q-values and recovers a posterior over rewards, including from raw pixels.
The Bayesian inverse-reinforcement-learning method combines uncertainty estimates with experiments across grid worlds, Lunar Lander, highway driving and two Atari games. The authors call it the first Bayesian IRL method demonstrated from raw pixel observations; performance remains tied to the studied apprenticeship-learning settings.
Why it matters
QVIRL learns a variational distribution over optimal Q-values and recovers a posterior over rewards, including from raw pixels.
Limits and context
No additional limitation was separately recorded.
Key claims
QVIRL learns a variational distribution over optimal Q-values and recovers a posterior over rewards, including from raw pixels.
Evidence: source-2026-08-18-017
Sources
- arXiv preprint 2608.16888arXiv · primary research
Corrections
No corrections have been recorded for this story.