reiniscimurs/DRL-robot-navigation
DRL Robot Navigation
DRL-robot-navigation is a deep reinforcement learning platform and robotic simulation framework designed to train autonomous mobile robots for collision-free path planning. It uses neural network policies and physics-engine simulation environments to teach robots how to navigate toward target coordinates while avoiding obstacles.
The software trains continuous control policies using twin delayed deep deterministic policy gradients over continuous state and action spaces. Training is guided by scalar reward signals derived from target proximity and obstacle avoidance distances. System components communicate via asynchronous publish-subscribe messaging topics to route sensor inputs, motor commands, and learning states.
The platform runs inside isolated headless containers to encapsulate the simulation and machine learning pipeline for consistent multi-platform execution.
Features
- Reinforcement Learning Environments - Provides a machine learning environment built with neural networks for training autonomous systems.
- Continuous Control Training - Trains control agents across continuous state and action spaces for mobile robot navigation.
- Deep Reinforcement Learning Implementations - Trains mobile robots using deep reinforcement learning algorithms for collision-free path planning.
- Goal-Directed - Directs reinforcement learning optimization through scalar feedback based on target proximity.
- Reward Functions - Evaluates neural network performance using target proximity and obstacle avoidance reward signals.
- Policy Gradient Optimizers - Optimizes continuous robot control policies using twin delayed deep deterministic policy gradient algorithms.
- Reinforcement Learning - Employs neural network policies to train autonomous agents for collision-free path planning.
- ROS Simulation Bridges - Utilizes a robotics simulation framework to train autonomous mobile robots using deep reinforcement learning.
- Autonomous Robot Navigation - Trains mobile robots to reach random goal coordinates while avoiding obstacles.
- Robot Operating System (ROS) Integrations - Interconnects simulated sensors, actuators, and learning nodes using robotic middleware communication topics.
- Robotic Physics and Sensor Simulators - Generates realistic three-dimensional sensor data and physical interactions using robotics simulation.
- Asynchronous Message Passings - Decouples system components by routing sensor inputs and motor commands over asynchronous channels.
- Containerized Robotics Stacks - Executes machine learning training and physics simulations within containerized robotic workflows.
