- AutonomousRobot-v0: Discrete Action Space, Infraredsensor and Ultrasonic Sensor, Random Spawns
- AutonomousRobot-v1: Continuous Action Space, Infraredsensor and Ultrasonic Sensor, Random Spawns
- AutonomousRobot-v2: Discrete Observation Space, Discrete Action Space (for Q-Learning), Infrared & Ultrasonic Sensor
- AutonomousRobot-v3: Discrete Action Space, Target Position, Random Spawns, 3 Distance Sensors (Basically Scenario 3 from the Thesis)
Each Environment can of course be customized indivually.
- Clone the Repo
- Install OpenAI Gym: https://github.com/openai/gym
- Run:
sudo pip install -r requirements
sudo python setup.py install
OR: Use the Dockerfile to create a Container
Then you should be ready to use the Environments by import gym_robot:
import gym_robot
The Repository comes with a few extas:
-
Few Agents: DDPG, DQN, DQN (With Keras-RL), Q-Learning, A3C(not really threaded), Keyboard Agent (can be handy when designing environments)
-
A Dockerfile
-
2 Scripts for visualizing logfiles:
- for keras-rl logs:
visualize.py filename.ending
- for csv files:
cs.py filename.ending
-
Some pretrained Models
- The simulation is located in gym_robot/envs/obstacles.py
- Got 2 classes: one for obstacles and one for the robot itself
- The Methods for the sensors are in robot class
- Rendering is done by OpenAI Gym Renderer in the specific environment
- test.py contains unit tests for the classes