# PPO Dash: Improving Generalization in Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1907.06704 · HTML: https://mlsanity.com/paper/1907.06704/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

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

- [Autonomous Payload Thermal Control](https://mlsanity.com/paper/2307.15438/) (2023)

## Abstract (arXiv)

Deep reinforcement learning is prone to overfitting, and traditional benchmarks such as Atari 2600 benchmark can exacerbate this problem. The Obstacle Tower Challenge addresses this by using randomized environments and separate seeds for training, validation, and test runs. This paper examines various improvements and best practices to the PPO algorithm using the Obstacle Tower Challenge to empirically study their impact with regards to generalization. Our experiments show that the combination provides state-of-the-art performance on the Obstacle Tower Challenge.