This environment is called the Reacher Environment. It consists of a double-jointed arm can move to target locations. When the agent's hand is on the target location, it receives a reward of +0.1. The state-space consists of 33 variables corresponding to the arm's position, rotation, velocity, and angular velocities. The action space is a 4 number vector representing torque applicable to two joints, and every entry in the action vector should be a number between -1 and 1. The environment is solved once the agent receives an average score of 30 over 100 consecutive episodes. Currently, robotic arms in factories require a very tightly controlled environment. This technology can serve as a solution to more versatile machinery.
Linux: https://s3-us-west-1.amazonaws.com/udacity-drlnd/P2/Reacher/one_agent/Reacher_Linux.zip
Mac OSX: https://s3-us-west-1.amazonaws.com/udacity-drlnd/P2/Reacher/one_agent/Reacher.app.zip
Windows (32-bit): https://s3-us-west-1.amazonaws.com/udacity-drlnd/P2/Reacher/one_agent/Reacher_Windows_x86.zip
Windows (64-bit): https://s3-us-west-1.amazonaws.com/udacity-drlnd/P2/Reacher/one_agent/Reacher_Windows_x86_64.zip
tensorflow==1.7.1
Pillow>=4.2.1
matplotlib
numpy>=1.11.0
jupyter
pytest>=3.2.2
docopt
pyyaml
protobuf==3.5.2
grpcio==1.11.0
torch>=1.0.0
pandas
scipy
ipykernel
In order to run the project, go to Continuous_Contol.ipynb. From there, run all of the code cells.
The code was written making a references to: GitHub. 2022. ContinousControl/Continuous_Control.ipynb at master · gkowalik/ContinousControl. [online] Available at: https://github.com/gkowalik/ContinousControl/blob/master/Continuous_Control.ipynb [Accessed 10 May 2022].