**arXiv ID:** 1602.02867 **Authors:** Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel **Published:** 2016-02-09T05:44:36Z **Abstract:** We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the v...
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# Value Iteration Networks
**arXiv ID:** 1602.02867
**Authors:** Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel
**Published:** 2016-02-09T05:44:36Z
**Abstract:**
We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation. We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task. We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.
## Skill Description
This skill is generated from the arXiv paper: Value Iteration Networks (1602.02867).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:1602.02867](http://arxiv.org/abs/1602.02867v4)
