AI Insight
This paper introduces Relative Value Learning (RV), a new reinforcement learning framework that directly learns differences between state values rather than absolute values. The authors prove that their pairwise Bellman operator is mathematically sound and demonstrate that their method achieves competitive performance with standard Proximal Policy Optimization (PPO) on 49 Atari games. The framework includes a new advantage estimation method (R-GAE) that provides unbiased policy gradient estimates while operating on value differences.
Why it matters
This approach could improve the efficiency and stability of reinforcement learning algorithms by focusing on the value differences that directly inform decision-making rather than absolute value estimates. The competitive performance on Atari benchmarks suggests this could be a practical alternative for developing AI systems in robotics, game playing, and other sequential decision-making applications.
Understand the Science
arXiv:2607.21120v1 Announce Type: cross
Abstract: In reinforcement learning, critics typically estimate absolute state values $V(s)$, estimating how good a particular situation is in isolation. However, it turns out that only differences in value are relevant for control. Motivated by this, we propose Relative Value Learning (RV), a framework that learns value differences directly via an antisymmetric function $Delta(s_i, s_j) = V(s_i) – V(s_j)$. We introduce a pairwise Bellman operator and prove it is a $gamma$-contraction with a unique fixed point equal to the true value differences, derive well-posed $1$-step, $n$-step and $lambda$-return targets and reconstruct generalized advantage estimation from pairwise differences to obtain an unbiased policy-gradient estimator (R-GAE). Beyond theoretical results, we integrate RV with PPO and achieve competitive performance on the Atari benchmark (49 ALE games) compared to standard PPO, indicating that relative value estimation is an effective alternative to absolute critics.
Source: Relative Value Learning