Combining Modelfree Qensembles And Modelbased Approaches For Informed Exploration

# Combining Model-Free Q-Ensembles and Model-Based Approaches for Informed Exploration

**arXiv ID:** 1806.04552
**Authors:** Sreecharan Sankaranarayanan, Raghuram Mandyam Annasamy, Katia Sycara, Carolyn Penstein Rosé
**Published:** 2018-06-12T14:24:02Z
**Abstract:**
Q-Ensembles are a model-free approach where input images are fed into different Q-networks and exploration is driven by the assumption that uncertainty is proportional to the variance of the output Q-values obtained. They have been shown to perform relatively well compared to other exploration strategies. Further, model-based approaches, such as encoder-decoder models have been used successfully for next frame prediction given previous frames. This paper proposes to integrate the model-free Q-ensembles and model-based approaches with the hope of compounding the benefits of both and achieving superior exploration as a result. Results show that a model-based trajectory memory approach when combined with Q-ensembles produces superior performance when compared to only using Q-ensembles.

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## References

- [arXiv:1806.04552](http://arxiv.org/abs/1806.04552v1)