# Observational Learning by Reinforcement Learning

arXiv: https://arxiv.org/abs/1706.06617 · HTML: https://mlsanity.com/paper/1706.06617/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2017.

## Built on by (most cited first, 12 of 12)

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- [Learning Robust Real-Time Cultural Transmission without Human Data](https://mlsanity.com/paper/2203.00715/) (2022)
- [Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning](https://mlsanity.com/paper/2406.00392/) (2024)
- [Generative AI collective behavior needs an interactionist paradigm](https://mlsanity.com/paper/2601.10567/) (2026)
- [Basis for Intentions: Efficient Inverse Reinforcement Learning using Past Experience](https://mlsanity.com/paper/2208.04919/) (2022)
- [Optimising Long-Term Outcomes using Real-World Fluent Objectives: An Application to Football](https://mlsanity.com/paper/2102.09469/) (2021)
- [Learning to Learn from Language Feedback with Social Meta-Learning](https://mlsanity.com/paper/2602.16488/) (2026)
- [HiMA: A Fast and Scalable History-based Memory Access Engine for Differentiable Neural Computer](https://mlsanity.com/paper/2202.07275/) (2022)
- [Exploiting Expertise of Non-Expert and Diverse Agents in Social Bandit Learning: A Free Energy Approach](https://mlsanity.com/paper/2603.11757/) (2026)
- [Artifacts as Memory Beyond the Agent Boundary](https://mlsanity.com/paper/2604.08756/) (2026)

## Abstract (arXiv)

Observational learning is a type of learning that occurs as a function of observing, retaining and possibly replicating or imitating the behaviour of another agent. It is a core mechanism appearing in various instances of social learning and has been found to be employed in several intelligent species, including humans. In this paper, we investigate to what extent the explicit modelling of other agents is necessary to achieve observational learning through machine learning. Especially, we argue that observational learning can emerge from pure Reinforcement Learning (RL), potentially coupled with memory. Through simple scenarios, we demonstrate that an RL agent can leverage the information provided by the observations of an other agent performing a task in a shared environment. The other agent is only observed through the effect of its actions on the environment and never explicitly modeled. Two key aspects are borrowed from observational learning: i) the observer behaviour needs to change as a result of viewing a 'teacher' (another agent) and ii) the observer needs to be motivated somehow to engage in making use of the other agent's behaviour. The later is naturally modeled by RL, by correlating the learning agent's reward with the teacher agent's behaviour.