MOR

Multi-Objective Robotics

Description

Many real-world problems have conflicting objectives, however, it is difficult to design a single reward function that optimally combines all objectives. To address this, we will simultaneously and independently optimize all solutions on the Pareto front. This project will investigate how to implement and improve the existing MO-CMA-ES algorithm to operate a Baxter robot quickly and adaptively in production for a multi-objective problem such as collision avoidance.

Run NES Algorithm

This will run the default NES Algorithm with the parameters in Config.yaml

Local Machine

1. Clone this directory
2. cd MOR/
3. Run python train <CONFIG_FILENAME>.yaml to run the algorithm in the foreground (append an & at the end to run in the background)
   • Use <CONFIG_FILENAME> = "Config" to run the default Maze example
   • Check other config files in cfg/ for other options, or write your own .yaml config file and add it to cfg/.
   • Resolve any dependecy issues that may arise
     • Linux/Mac OSX: sudo -H pip install numpy tensorflow matplotlib pyyaml

Results

• Check the ext/ directory for your output data
• The .log file contains the problem state, the reward function, and the results of each individual of each population.
• The .yaml file contains the parameterws used during training
• The .png files are graphs of the rewards/success per population.