Dual Advantage Fields converts bilinear dual value models into local advantage scores via learned action-effect models, equaling the goal-conditioned Bellman advantage under realizability and improving aggregate metrics on OGBench locomotion, manipulation, and puzzle tasks.
Learning temporal distances: Contrastive successor features can provide a metric structure for decision-making
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abstract
Temporal distances lie at the heart of many algorithms for planning, control, and reinforcement learning that involve reaching goals, allowing one to estimate the transit time between two states. However, prior attempts to define such temporal distances in stochastic settings have been stymied by an important limitation: these prior approaches do not satisfy the triangle inequality. This is not merely a definitional concern, but translates to an inability to generalize and find shortest paths. In this paper, we build on prior work in contrastive learning and quasimetrics to show how successor features learned by contrastive learning (after a change of variables) form a temporal distance that does satisfy the triangle inequality, even in stochastic settings. Importantly, this temporal distance is computationally efficient to estimate, even in high-dimensional and stochastic settings. Experiments in controlled settings and benchmark suites demonstrate that an RL algorithm based on these new temporal distances exhibits combinatorial generalization (i.e., "stitching") and can sometimes learn more quickly than prior methods, including those based on quasimetrics.
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A quality-aware exploration method using return-conditioned sigmoid scheduling and per-agent RSQ metrics achieves top-tier returns on seven cooperative MARL benchmarks.
Normalizing-flow subgoal policies plus triangle-slack reweighting provably avoid Gaussian mode-averaging and filter lucky transitions in offline hierarchical GCRL.
Proposes mean flow policies and LeJEPA loss to overcome Gaussian policy limits and weak subgoal generation in hierarchical offline GCRL, reporting strong results on OGBench state and pixel tasks.
citing papers explorer
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Dual Advantage Fields
Dual Advantage Fields converts bilinear dual value models into local advantage scores via learned action-effect models, equaling the goal-conditioned Bellman advantage under realizability and improving aggregate metrics on OGBench locomotion, manipulation, and puzzle tasks.
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Quality-Aware Exploration Budget Allocation for Cooperative Multi-Agent Reinforcement Learning
A quality-aware exploration method using return-conditioned sigmoid scheduling and per-agent RSQ metrics achieves top-tier returns on seven cooperative MARL benchmarks.
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NFTR: From Provable Mode-Averaging to Geodesic Subgoal Selection in Offline Goal-Conditioned RL
Normalizing-flow subgoal policies plus triangle-slack reweighting provably avoid Gaussian mode-averaging and filter lucky transitions in offline hierarchical GCRL.
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Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning
Proposes mean flow policies and LeJEPA loss to overcome Gaussian policy limits and weak subgoal generation in hierarchical offline GCRL, reporting strong results on OGBench state and pixel tasks.