It's a matter of timescale: non-linear utility in successor features and multi-objective planning and learning

arXiv:2608.25723 · cs.LG, cs.AI · Submitted 2026-08-26 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-08-26

Updated: 2026-08-31

Code: https://github.com/liammertens/MORL-timescale

Terminology

• Reinforcement Learning — 25× in this paper · explained in 4 episodes
• Reward Function — 13× in this paper · explained in 2 episodes
• Multi-Agent System — 3× in this paper · explained in 6 episodes
• Option Keyboard — 3× in this paper
• Action Space — 2× in this paper · explained in 1 episode
• Actor — 2× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Expected Utility Policy Gradient — 1× in this paper
• Forward Backward — 1× in this paper
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Multi-objective Optimization — 1× in this paper · explained in 4 episodes
• Pruning — 1× in this paper · explained in 4 episodes

Sources

• Soft Forward-Backward Representations for Zero-shot Reinforcement Learning with General Utilities

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