# ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning, & Snapshot Ensembling

arXiv: https://arxiv.org/abs/1801.01000 · HTML: https://mlsanity.com/paper/1801.01000/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2018.

## Built on by (most cited first, 4 of 4)

- [Improved Soft Actor-Critic: Mixing Prioritized Off-Policy Samples with On-Policy Experience](https://mlsanity.com/paper/2109.11767/) (2021)
- [Lifetime policy reuse and the importance of task capacity](https://mlsanity.com/paper/2106.01741/) (2021)
- [The challenge of redundancy on multi-agent value factorisation](https://mlsanity.com/paper/2304.00009/) (2023)
- [A computational approach to visual ecology with deep reinforcement learning](https://mlsanity.com/paper/2402.05266/) (2024)

## Abstract (arXiv)

ViZDoom is a robust, first-person shooter reinforcement learning environment, characterized by a significant degree of latent state information. In this paper, double-Q learning and prioritized experience replay methods are tested under a certain ViZDoom combat scenario using a competitive deep recurrent Q-network (DRQN) architecture. In addition, an ensembling technique known as snapshot ensembling is employed using a specific annealed learning rate to observe differences in ensembling efficacy under these two methods. Annealed learning rates are important in general to the training of deep neural network models, as they shake up the status-quo and counter a model's tending towards local optima. While both variants show performance exceeding those of built-in AI agents of the game, the known stabilizing effects of double-Q learning are illustrated, and priority experience replay is again validated in its usefulness by showing immediate results early on in agent development, with the caveat that value overestimation is accelerated in this case. In addition, some unique behaviors are observed to develop for priority experience replay (PER) and double-Q (DDQ) variants, and snapshot ensembling of both PER and DDQ proves a valuable method for improving performance of the ViZDoom Marine.