# Curiosity-Driven Exploration by Self-Supervised Prediction

- **Authors**: Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell
- **Year**: 2017
- **Summary**: This paper introduces an approach to reinforcement learning where an agent's curiosity is the only reward signal. The curiosity is modeled as the error in the agent's ability to predict the consequence of its own actions. By being rewarded for visiting states that are surprising or hard to predict, the agent is intrinsically motivated to explore its environment and learn useful skills without any external rewards.
- **Link**: https://arxiv.org/abs/1705.05363
