Relative Value Learning

2026-07-23 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, quick

Summary

Relative Value Learning (RV) is a novel reinforcement learning framework introduced on July 23, 2026, designed to directly learn value differences between states rather than estimating absolute state values, which is typical for critics. This approach is motivated by the observation that only value differences are pertinent for control in reinforcement learning. RV employs an antisymmetric function, Δ(s_i, s_j) = V(s_i) - V(s_j), to model these differences. The framework includes a new pairwise Bellman operator, proven to be a γ-contraction with a unique fixed point representing true value differences. It also derives well-posed 1-step, n-step, and λ-return targets and reconstructs generalized advantage estimation (GAE) into R-GAE for an unbiased policy-gradient estimator. When integrated with PPO, RV demonstrated competitive performance across 49 Atari benchmark games compared to standard PPO, validating its effectiveness as an alternative to absolute critics.

Key takeaway

For Machine Learning Engineers optimizing reinforcement learning agents, consider adopting Relative Value Learning (RV) to simplify value estimation. By directly learning value differences instead of absolute state values, you can potentially achieve competitive performance with a more focused approach. Integrate RV with your existing PPO implementations and utilize R-GAE for robust policy gradient estimation. This framework offers a theoretically sound and practically effective alternative to traditional absolute critics.

Key insights

Relative Value Learning directly estimates state value differences, simplifying control in reinforcement learning.

Principles

• Only value differences are relevant for control.
• Pairwise Bellman operator ensures unique fixed point.
• Antisymmetric functions model value differences.

Method

Learn value differences directly via an antisymmetric function Δ(s_i, s_j) = V(s_i) - V(s_j), derive specific Bellman targets, and reconstruct GAE into R-GAE for policy gradients.

In practice

• Integrate RV with PPO for competitive performance.
• Apply R-GAE for unbiased policy gradients.
• Evaluate on Atari benchmark (49 games).

Topics

• Relative Value Learning
• Reinforcement Learning
• Value Estimation
• Policy Gradients
• PPO
• Atari Benchmark

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.