earticle

영어

This study investigates the emergence of emotional dynamics and intrinsic motivation within multi-agent reinforcement learning (MARL) systems. Traditional MARL frameworks rely solely on extrinsic task rewards, which often limit exploration and adaptability. To address this, we propose an Affective-Motivated MARL (AM-MARL) framework where agents integrate curiosity-based intrinsic rewards and emotionmodulated affective feedback alongside extrinsic reinforcement. Agents operate in a continuous multiagent environment, learning through Q-learning, Actor- Critic, or Advantage Actor-Critic (A2C) methods depending on their action space. The intrinsic reward is defined as the state-prediction error between observed and expected future states, while the affective reward arises from temporal changes in emotional state and the social influence among peers. Experimental results show that incorporating intrinsic and affective rewards enhances exploration coverage, stabilizes emotional trajectories, and improves coordination efficiency compared to extrinsic-only baselines. These findings suggest that emotional feedback, when coupled with curiosity-driven intrinsic signals, fosters more humanlike adaptability, cooperative intelligence, and stable affect regulation in MARL environments.