# Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning

arXiv: https://arxiv.org/abs/1612.07548 · HTML: https://mlsanity.com/paper/1612.07548/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2016.

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

- [Value Improved Actor Critic Algorithms](https://mlsanity.com/paper/2406.01423/) (2024)

## Abstract (arXiv)

This paper investigates a type of instability that is linked to the greedy policy improvement in approximated reinforcement learning. We show empirically that non-deterministic policy improvement can stabilize methods like LSPI by controlling the improvements' stochasticity. Additionally we show that a suitable representation of the value function also stabilizes the solution to some degree. The presented approach is simple and should also be easily transferable to more sophisticated algorithms like deep reinforcement learning.