# Transformable Gaussian Reward Function for Socially-Aware Navigation with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2402.14569 · HTML: https://mlsanity.com/paper/2402.14569/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 14 of 14)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning](https://mlsanity.com/paper/2010.03950/) (2020)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Using Natural Language for Reward Shaping in Reinforcement Learning](https://mlsanity.com/paper/1903.02020/) (2019)
- [Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping](https://mlsanity.com/paper/2011.02669/) (2020)
- [Quantifying Differences in Reward Functions](https://mlsanity.com/paper/2006.13900/) (2020)
- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2011.04820/) (2020)
- [Average-Reward Off-Policy Policy Evaluation with Function Approximation](https://mlsanity.com/paper/2101.02808/) (2021)
- [Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning](https://mlsanity.com/paper/2010.08600/) (2020)
- [Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation in Dense Mobile Crowds](https://mlsanity.com/paper/2010.14838/) (2020)
- [Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning](https://mlsanity.com/paper/2301.10886/) (2023)
- [Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction](https://mlsanity.com/paper/2107.07056/) (2021)
- [Reward Shaping for Human Learning via Inverse Reinforcement Learning](https://mlsanity.com/paper/2002.10904/) (2020)

## Built on by (most cited first, 3 of 3)

- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [Belief Aided Navigation using Bayesian Reinforcement Learning for Avoiding Humans in Blind Spots](https://mlsanity.com/paper/2403.10105/) (2024)
- [Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response](https://mlsanity.com/paper/2609.10433/) (2026)

## Abstract (arXiv)

Robot navigation has transitioned from prioritizing obstacle avoidance to adopting socially aware navigation strategies that accommodate human presence. As a result, the recognition of socially aware navigation within dynamic human-centric environments has gained prominence in the field of robotics. Although reinforcement learning technique has fostered the advancement of socially aware navigation, defining appropriate reward functions, especially in congested environments, has posed a significant challenge. These rewards, crucial in guiding robot actions, demand intricate human-crafted design due to their complex nature and inability to be automatically set. The multitude of manually designed rewards poses issues with hyperparameter redundancy, imbalance, and inadequate representation of unique object characteristics. To address these challenges, we introduce a transformable gaussian reward function (TGRF). The TGRF significantly reduces the burden of hyperparameter tuning, displays adaptability across various reward functions, and demonstrates accelerated learning rates, particularly excelling in crowded environments utilizing deep reinforcement learning (DRL). We introduce and validate TGRF through sections highlighting its conceptual background, characteristics, experiments, and real-world application, paving the way for a more effective and adaptable approach in robotics.The complete source code is available on https://github.com/JinnnK/TGRF