Social and emotional learning in artificial agents

Social and emotional intelligence are fundamental to human cognition, yet current artificial agent frameworks typically treat these capabilities separately, limiting their ability to generate authentic social interactions. We present SELAgents (Social and Emotional Learning Agents), a novel framework that integrates emotional processing, theory of mind, and social learning within a unified reinforcement learning architecture. The framework combines a three-dimensional emotional state space (Pleasure-Arousal-Dominance model), Bayesian belief networks for mental state inference, and game-theoretic social strategy selection. Through systematic experiments with populations of 10 heterogeneous agents over 200 timesteps (30 independent runs), we demonstrate significant improvements over traditional reinforcement learning baselines: emotional intelligence scores increased by 49% (0. 73 ± 0. 08 vs 0. 49 ± 0. 11, p < 0. 001), social coherence improved by 66% (0. 68 vs 0. 41, p < 0. 001), and resource allocation efficiency reached 87% (vs 62% baseline, p < 0. 001). Agents exhibited emergent behaviors including emotional contagion effects (correlation strength = 0. 72 in dense networks) and stable coalition formation (4. 3 ± 1. 2 agents per coalition). Ablation studies revealed that theory of mind capabilities contributed most significantly to performance (31. 2% degradation when removed), followed by emotional processing (28. 7%) and social strategies (22. 4%). These results suggest that integrating emotional processing with social learning mechanisms produces more sophisticated agent behaviors that exhibit patterns consistent with human social dynamics. We provide our complete implementation as open-source software to facilitate further research. This study assumes perfect observability of emotional states, representing an upper bound on achievable performance; extending the framework to partial observability settings remains an important direction for future work.