Mobile robot control using Deep Reinforcement Learning

Short description

Following project was a part of my master thesis. Current version is a little bit modified and much improved. The project is using modified algorithm Deterministic Policy Gradient (Lillicrap et al.arXiv:1509.02971) (written in Tensorflow) to control mobile robot. The main idea was to learn mobile robot navigate to goal and also avoid obstacles. For obstacles avoidance, robot is using 5 ultrasonic sensors. For navigation task, required information (like absolute pose) are taken from simulation engine. That's a little hack. However, more realistic (like odometry) pose estimation can be found in gym-vrep.

How to use

Dependencies

Simulation

Project is using robotic simulation V-REP EDU. To setup V-REP environment follow instruction described in gym-vrep repository.

Python

All Python dependencies are in requirements.txt. Just run command:

pip install -r requirements.txt

Running

To run training just run command:

python main.py --train

To run testing just run command:

python main.py

For more options (like state, action normalization) run command:

python main.py --help

Result

Agent was learned for 1000 episodes and results are shown below: