# Pathfinding in Random Partially Observable Environments with Vision-Informed Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2209.04801 · HTML: https://mlsanity.com/paper/2209.04801/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 11 Sep 2022.

## Builds on (most cited first, 3 of 3)

- [PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning](https://mlsanity.com/paper/1809.03531/) (2018)
- [PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning](https://mlsanity.com/paper/1710.03937/) (2017)
- [A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments](https://mlsanity.com/paper/2005.10619/) (2020)

## Abstract (arXiv)

Deep reinforcement learning is a technique for solving problems in a variety of environments, ranging from Atari video games to stock trading. This method leverages deep neural network models to make decisions based on observations of a given environment with the goal of maximizing a reward function that can incorporate cost and rewards for reaching goals. With the aim of pathfinding, reward conditions can include reaching a specified target area along with costs for movement. In this work, multiple Deep Q-Network (DQN) agents are trained to operate in a partially observable environment with the goal of reaching a target zone in minimal travel time. The agent operates based on a visual representation of its surroundings, and thus has a restricted capability to observe the environment. A comparison between DQN, DQN-GRU, and DQN-LSTM is performed to examine each models capabilities with two different types of input. Through this evaluation, it is been shown that with equivalent training and analogous model architectures, a DQN model is able to outperform its recurrent counterparts.