A unified view of ARD in RL is provided by introducing a taxonomy, organized by the primary driver of the reward variation, that distinguishes external-feedback-driven reward updates from reward adaptations driven by endogenous within-run signals and those conditioned on exogenous context signals.
A unified analytical framework for comparing dynamic reward shaping and neighbouring adaptive reward mechanisms is introduced, which distinguishes parametric revision from state-dependent variation, separates additive shaping from reward replacement and reward-adjacent guidance, and organises existing methods along temporal, informational, and theoretical dimensions.
This work proposes LaRes, a novel hybrid framework that achieves efficient policy learning through reward function search by leveraging large language models to generate the reward function population, guiding RL in policy learning.
Pengyi Li, Hongyao Tang, Jinbin Qiao et al.· Neural Information Processin...· 4 citations
MeRLa (Meta-Learned Reward Shaping), a principled framework that meta-learns a task-aware shaping function across auxiliary tasks before RLHF training, is introduced, providing theoretical guarantees for policy invariance, analyze representation drift sensitivity, and formally address incentive misalignment from entropy maximization.
This work introduces Gradient Control Rewards (GCR), an interpretable, control-inspired reward-design methodology for accelerating agent training by modulating the reward signal based on the temporal dynamics of system error, Inspired by classical control theory.
Anas Aburaya, H. Selamat, M. Muslim et al.· IEEE Access· 0 citations
A coherent map of the rapidly expanding landscape of visual RL is provided to provide researchers and practitioners with a coherent map of the rapidly expanding landscape of visual RL and to highlight promising directions for future inquiry.
This work develops LEMUR: Learning to Align with Multi-Objective Reinforcement Learning with Preference feedback, a novel framework where an agent interactively learns from the preferences of multiple humans to learn optimal multi-objective policies.
Manith Adikari, Bei Peng, Samuele Vinanzi et al.· arXiv.org· 0 citations
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