# Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning

arXiv: https://arxiv.org/abs/1705.03562 · HTML: https://mlsanity.com/paper/1705.03562/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2017.

## Built on by (most cited first, 1 of 1)

- [DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs](https://mlsanity.com/paper/2010.08891/) (2020)

## Abstract (arXiv)

We present a new deep meta reinforcement learner, which we call Deep Episodic Value Iteration (DEVI). DEVI uses a deep neural network to learn a similarity metric for a non-parametric model-based reinforcement learning algorithm. Our model is trained end-to-end via back-propagation. Despite being trained using the model-free Q-learning objective, we show that DEVI's model-based internal structure provides `one-shot' transfer to changes in reward and transition structure, even for tasks with very high-dimensional state spaces.