# Emergent Social Learning via Multi-agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2010.00581 · HTML: https://mlsanity.com/paper/2010.00581/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 30 of 44)

- [Celebrating Diversity in Shared Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2106.02195/) (2021)
- [Multi-agent Embodied AI: Advances and Future Directions](https://mlsanity.com/paper/2505.05108/) (2025)
- [Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Values](https://mlsanity.com/paper/2110.01307/) (2021)
- [The SocialAI School: Insights from Developmental Psychology Towards Artificial Socio-Cultural Agents](https://mlsanity.com/paper/2307.07871/) (2023)
- [Sotopia-RL: Reward Design for Social Intelligence](https://mlsanity.com/paper/2508.03905/) (2025)
- [A social path to human-like artificial intelligence](https://mlsanity.com/paper/2405.15815/) (2024)
- [Pretraining in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2211.03959/) (2022)
- [ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward](https://mlsanity.com/paper/2210.04365/) (2022)
- [Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2210.03022/) (2022)
- [RILI: Robustly Influencing Latent Intent](https://mlsanity.com/paper/2203.12705/) (2022)
- [Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning](https://mlsanity.com/paper/2406.00392/) (2024)
- [Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds](https://mlsanity.com/paper/2405.16439/) (2024)
- [Generative AI collective behavior needs an interactionist paradigm](https://mlsanity.com/paper/2601.10567/) (2026)
- [Learning Latent Representations to Co-Adapt to Humans](https://mlsanity.com/paper/2212.09586/) (2022)
- [iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.06236/) (2023)
- [Improving Interactive In-Context Learning from Natural Language Feedback](https://mlsanity.com/paper/2602.16066/) (2026)
- [SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution](https://mlsanity.com/paper/2604.18982/) (2026)
- [Learning to Guide and to Be Guided in the Architect-Builder Problem](https://mlsanity.com/paper/2112.07342/) (2021)
- [Social Neuro AI: Social Interaction as the "dark matter" of AI](https://mlsanity.com/paper/2112.15459/) (2021)
- [Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning](https://mlsanity.com/paper/2204.12371/) (2022)
- [On the Role of Emergent Communication for Social Learning in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2302.14276/) (2023)
- [Decentralized Multi-Agent Reinforcement Learning with Global State Prediction](https://mlsanity.com/paper/2306.12926/) (2023)
- [An Efficient Open World Environment for Multi-Agent Social Learning](https://mlsanity.com/paper/2508.15679/) (2025)
- [Learning Connectivity-Maximizing Network Configurations](https://mlsanity.com/paper/2112.07663/) (2021)
- [A framework for the emergence and analysis of language in social learning agents](https://mlsanity.com/paper/2305.02632/) (2023)
- [Understanding the World to Solve Social Dilemmas Using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2305.11358/) (2023)
- [Synthetic Social Media Influence Experimentation via an Agentic Reinforcement Learning Large Language Model Bot](https://mlsanity.com/paper/2411.19635/) (2024)
- [Unifying Agent Interaction and World Information for Multi-agent Coordination](https://mlsanity.com/paper/2509.25550/) (2025)
- [Collaborative QA using Interacting LLMs. Impact of Network Structure, Node Capability and Distributed Data](https://mlsanity.com/paper/2511.14098/) (2025)
- [Group-Agent Reinforcement Learning](https://mlsanity.com/paper/2202.05135/) (2022)

## Abstract (arXiv)

Social learning is a key component of human and animal intelligence. By taking cues from the behavior of experts in their environment, social learners can acquire sophisticated behavior and rapidly adapt to new circumstances. This paper investigates whether independent reinforcement learning (RL) agents in a multi-agent environment can learn to use social learning to improve their performance. We find that in most circumstances, vanilla model-free RL agents do not use social learning. We analyze the reasons for this deficiency, and show that by imposing constraints on the training environment and introducing a model-based auxiliary loss we are able to obtain generalized social learning policies which enable agents to: i) discover complex skills that are not learned from single-agent training, and ii) adapt online to novel environments by taking cues from experts present in the new environment. In contrast, agents trained with model-free RL or imitation learning generalize poorly and do not succeed in the transfer tasks. By mixing multi-agent and solo training, we can obtain agents that use social learning to gain skills that they can deploy when alone, even out-performing agents trained alone from the start.