Project 2 "Continuous Control" of the Deep Reinforcement Learning nanodegree.
You can find the training code here: Continuous_Control.ipynb, ddpg_agent.py, and model.py.
You can find the saved model weights here: checkpoint_actor.pth and checkpoint_critic.pth.
For this project, you will work with the Reacher environment.
In this environment, a double-jointed arm can move to target locations. A reward of +0.1 is provided for each step that the agent's hand is in the goal location. Thus, the goal of your agent is to maintain its position at the target location for as many time steps as possible.
The observation space consists of 33 variables corresponding to position, rotation, velocity, and angular velocities of the arm. Each action is a vector with four numbers, corresponding to torque applicable to two joints. Every entry in the action vector should be a number between -1 and 1.
Option 1: Solve the First Version
The task is episodic, and in order to solve the environment, your agent must get an average score of +30 over 100 consecutive episodes. LIKE THIS:
Follow the instructions in this link in order to install all the dependencies required to run this project:
https://github.com/udacity/deep-reinforcement-learning#dependencies
Download the Project 2 - Continuous Control into your computer:
https://github.com/jckuri/DeepRL-Continuous-Control
Follow the instructions in this link in order to install the Unity environment required to run this project:
https://github.com/udacity/deep-reinforcement-learning/tree/master/p2_continuous-control#getting-started
The easiest way to install the requirements is to use the file requirements.txt
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==0.4.0
pandas
scipy
ipykernel
Execute this command in order to install the software specified in requirements.txt
pip -q install ./python
This command is executed at the beginning of the Jupyter notebook Continuous_Control.ipynb.
If you have troubles when installing this project, you can write me at:
https://www.linkedin.com/in/jckuri/
Follow the instructions in Continuous_Control.ipynb to get started with training your own agent!
To run the Jupyter notebook, use the following Unix command inside the project's directory:
jupyter notebook Continuous_Control.ipynb
To run all the cells in the Jupyter notebook again, go to the Jupyter notebook menu, and click on Kernel => Restart & Run All.
At the end of the Jupyter notebook, there is a space in which you can program your own implementation of this DDPG Agent.
You can find the report here: Report.md