Robot Learning

Distributional Reinforcement Learning

Distributional Reinforcement Learning is a family of value-based methods that model the full probability distribution of returns rather than only their expectation. Introduced with the C51 algorithm by Bellemare, Dabney, and Munos in 2017 and extended by quantile regression approaches such as QR-DQN and IQN, distributional critics often improve learning stability and enable risk-sensitive action selection based on return quantiles.

Why it matters for physical AI

Risk-aware control matters when failures break hardware or endanger people. Return distributions let deployed policies act conservatively in high-variance situations instead of optimizing average-case outcomes.