Udacity Deep Reinforcement Nanodegree Project 2: Continuous Control

This repository contains implementation of Continuous Control project as a part of Udacity's Deep Reinforcement Learning Nanodegree program.

In this project a double-jointed arm is trained to control its position for a moving target.

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, Rand 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.

The task is episodic, and in order to solve the environment, the agent must get an average score of +30 over 100 consecutive episodes.

Getting Started

Dependencies

To set up your python environment to run the code in this repository, follow the instructions below.

1. Install Anaconda for Python3 from here.

2. Create (and activate) a new environment with Python 3.6.

   • Linux or Mac:

   conda create --name p2_drlnd python=3.6
   source activate p2_drlnd

   • Windows:

   conda create --name p2_drlnd python=3.6 
   activate p2_drlnd

3. Follow the instructions in this repository to perform a minimal install of OpenAI gym.

4. Clone the repository (if you haven't already!), and navigate to the python/ folder. Then, install several dependencies.

git clone https://github.com/mayurand/deepRL-p2-Continuous-Control.git
cd deepRL-p2-Continuous-Control/python
pip install .

5. Create an IPython kernel for the p2_drlnd environment.

python -m ipykernel install --user --name p2_drlnd --display-name "p2_drlnd"

Environment setup

1. Download the environment from one of the links below. You need only select the environment that matches your operating system:
   • Linux: click here
   • Mac OSX: click here
   • Windows (32-bit): click here
   • Windows (64-bit): click here

(For Windows users) Check out this link if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system.

2. Place the file in the DRLND GitHub repository, in the p2_continuous-control/ folder, and unzip (or decompress) the file.

3. Test if the environment is correctly installed:

cd deepRL-p2-Continuous-Control/p2_continuous-control #navigate to the p2_continuous-control directory
source activate p2_drlnd  #Activate the python environment
jupyter notebook

4. Open the Test_the_environment.ipynb and run the cells with SHIFT+ENTER. If the environment is correctly installed, you should get to see the Unity environment in another window and values for state and action spaces under 2. Examine the State and Action Spaces.

Train the agent or run a trained agent

1. To train an agent for the above environment:

cd deepRL-p2-navigation/p2_continuous-control #navigate to the p2_continuous-control directory
source activate p2_drlnd  #Activate the python environment
jupyter notebook

2. Open the Continuous_Control.ipynb and run the cells with SHIFT+ENTER.

3. To directly run the trained model, navigate to the 4. Watch a Smart Agent! in the notebook and run the code.

Note: Before running code in a notebook, change the kernel to match the p2_drlnd environment by using the drop-down Kernel menu.

Report

See the report for more details on the implementation.