Policy Complexity, Reaction Time, and Bounded Rationality in Reinforcement Learning

arXiv:2609.28737 · cs.LG, cs.AI, cs.IT, math.IT · Submitted 2026-09-23 · Read on arXiv

cs.LG, cs.AI, cs.IT, math.IT

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Reinforcement Learning — 17× in this paper · explained in 4 episodes
• Regularization — 16× in this paper · explained in 3 episodes
• Actor — 7× in this paper · explained in 3 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• Generalization — 5× in this paper · explained in 13 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Q-learning — 3× in this paper · explained in 3 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Actor-Critic Learning — 2× in this paper · explained in 1 episode
• Computational Biology — 2× in this paper · explained in 2 episodes
• Resource Constraints — 2× in this paper · explained in 3 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Actor-Critic Model — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Gymnasium: A Standard Interface for Reinforcement Learning Environments

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: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks