The project code of course "Applied Reinforcement Learning" in Technical University of Munich Summer Semester 2020.
- B.Sc. Yikai Kang (03728450)
- B.Sc. Ming Gui (03687866)
- B.Sc. Bowen Ma (03721259)
In this Project, Reinforcement Learning is implemented to solve the task that robot base and robotic arms can get all scores in movement.
- Open main.py
- Run
- documentation: The figure of exponential reward and MATLAB code.
- code/maps: Path of robot is saved in maps.
- code/resources: Some image items which are read by Viewer class.
- code/misc: miscellaneous folder
- code/quick_start.py: "Hello World" file and run it directly.
- code/main.py: Clean py file to quick testing. It will import classes and functions from env.py, therefore this file is very clean and you can test your algorithm directly.
- code/reward_calculate.py: We provided different reward modes to test the performance of RL algorithm.
- code/rl.py: Reinforcement learning algorithm.
- code/elements.py: Save some global variables.
You may see 5 items in state list variable. For example, you can see the following numbers: [570.0, 200.00000000000165, 45, 45, (1, 1, 1)]
- x position of robot base
- y position
- Angle of arm1
- Angle of arm2
- Status of scores: 1 means it still exists, 0 means it have been collected.
Three different types of actions. The action variable is also a list. It looks like: [1,0,1]
- Robot base direction: 1 or 0 or -1
- Arm1 direction: 1 or 0 or -1
- Arm2 direction: 1 or 0 or -1 1 means move(rotate) forwards, 0 means don't move and -1 means move(rotate) backwards.