AdaGamma: State-Dependent Discounting for Temporal Adaptation in Reinforcement Learning

arXiv:2605.06149 · cs.LG, cs.AI · Submitted 2026-05-07 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-05-07

Updated: 2026-10-11

Comments: 29 pages, 6 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Actor — 2× in this paper · explained in 3 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes

Sources

• Gymnasium: A Standard Interface for Reinforcement Learning Environments
• Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks