# Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward Shaping

arXiv: https://arxiv.org/abs/2003.12863 · HTML: https://mlsanity.com/paper/2003.12863/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Multi-Phase Multi-Objective Dexterous Manipulation with Adaptive Hierarchical Curriculum](https://mlsanity.com/paper/2205.13441/) (2022)
- [Deep Reinforcement Learning with anticipatory reward in LSTM for Collision Avoidance of Mobile Robots](https://mlsanity.com/paper/2508.07941/) (2025)

## Abstract (arXiv)

In this paper, we investigate the obstacle avoidance and navigation problem in the robotic control area. For solving such a problem, we propose revised Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization algorithms with an improved reward shaping technique. We compare the performances between the original DDPG and PPO with the revised version of both on simulations with a real mobile robot and demonstrate that the proposed algorithms achieve better results.