The aim of this project is to train reinforcement learning agent for mobile robot control task. Project is based on ROS and Gazebo-gym environment for Turtlebot. Agents use turtlebot lidar sensors and information from A* global path planner as input.

• DQN - RL algorithm (done)
• Dynamic Window Approach - classic algorithm (done)
• Advantage Actor Critic - RL algorithm (in progress)

You can train agnets on the Turtlebot-Lidar enviroments. Project also contains Cartpole-Environment for debugging.

Before running you have to setup ros catkin workspace with Gazebo-gym (https://github.com/erlerobot/gym-gazebo) and activate it.

Project requires python environment with:

• Pytorch-ignite
• matplotlib
• OpenAI-gym
• h5py
• pyyaml

training:

python train.py --env=env-maze-v0 --eps-decay=1000 --net=lstm

evaluatiion:

python eval.py --agent=dqn --env=myenv-v0 --weights out/2019_05_04_17_17_51/model_1900_total_reward_30.5

This project is licensed under the MIT License.

• Inspiration - https://www.mdpi.com/2076-3417/9/7/1384