Discovery of the reward function for embodied reinforcement learning agents.

ABSTRACT: Reward maximization is a fundamental principle in both the survival and evolution of biological organisms. In particular, in the contexts of cognitive science and embodied agents, reward-driven behavior has been widely regarded as important in terms of governing complex cognitive abilities such as perception, imitation, and learning. Among the frameworks that are aimed at establishing such abilities, reinforcement learning (RL), which leverages reward maximization to facilitate intelligent decision-making in embodied agents, has been proven to be particularly promising. Importantly, the inherent complexity and uncertainty of real-world tasks pose significant challenges when designing effective reward functions for embodied RL agents. Conventional methods typically rely on manually engineer

SUBMITTER: Lu R 

PROVIDER: S-EPMC12698663 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature