Reinforcement Learning for Robot Navigation Author: Ali Zangeneh Email: engineer.zangeneh@gmail.com
GitHub: https://github.com/alizangeneh
Research Profile: https://orcid.org/0009-0002-5184-0571
Project Overview
This repository presents a research-grade reinforcement learning framework for robot navigation. The project is designed as a progressive and unified system that moves from:
Discrete single-agent navigation
To obstacle-aware environments
To continuous control
And finally to multi-agent cooperative navigation
The objective is to provide a full pipeline suitable for:
PhD-level research preparation
Robotics and autonomous systems
Reinforcement learning algorithm benchmarking
Scientific publication and reproducible experimentation
Core Research Contributions
This project is not a collection of toy examples. It is structured to demonstrate:
Reward-shaped navigation
Obstacle-aware path planning via RL
Continuous control using policy-gradient methods
Multi-agent cooperative learning
Reproducible training with fixed random seeds
Scientific evaluation with statistical metrics
Direct integration with LaTeX-based paper writing
Implemented Learning Paradigms
Tabular Reinforcement Learning
Q-Learning on GridWorld
Deep Reinforcement Learning
DQN on CartPole
PPO on navigation tasks
Continuous Control
PPO-based velocity control
Multi-Agent Reinforcement Learning
Cooperative navigation with shared team rewards
Environments
The project contains four navigation environments built on a shared base interface:
robot_nav_env.py Discrete, single-agent, no obstacles
robot_nav_env_obstacles.py Discrete, single-agent, obstacle-aware
robot_nav_env_continuous.py Continuous state and action control
robot_nav_env_multiagent.py Multi-robot cooperative navigation
All environments follow the Gymnasium API standard.
Algorithms
Q-Learning
Deep Q-Network (DQN)
Proximal Policy Optimization (PPO)
Multi-Agent PPO
Training and Evaluation Pipeline
The project supports:
Deterministic seeding
TensorBoard logging
Automated statistical evaluation
Mean and standard deviation reward analysis
Algorithm comparison plots
Model checkpointing
Visual rollout demonstrations
Reproducibility
All experiments use:
Fixed random seed
Deterministic environment initialization
Saved evaluation metrics
Fully reproducible training scripts
Scientific Reporting
The repository includes a full LaTeX paper template with:
Abstract
Introduction
Related Work
Method
Experiments
Results
Conclusion
Automated result table generation is also supported.
Target Audience
This project is intended for:
PhD applicants in AI, Robotics, and Control
Machine learning researchers
Robotics engineers
Reinforcement learning practitioners
License
This project is released under the MIT License.