Project 2: Continuous Control

Introduction

For this project, we 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.

Solving the Environment

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

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

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

- __Linux__ or __Mac__: 
```bash
conda create --name continous-control python=3.6
source activate continous-control
```
- __Windows__: 
```bash
conda create --name continous-control python=3.6 
activate continous-control
```

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

   • Next, install the classic control environment group by following the instructions here.
   • Then, install the box2d environment group by following the instructions here.

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

git clone https://github.com/Juenjie/Continous-Control.git
cd Continous-Control/python
pip install .

4. Create an IPython kernel for the Continous-Control environment.

python -m ipykernel install --user --name Continous-Control --display-name "Continous-Control"

5. Download the environment from one of the links below. You need only select the environment that matches your operating system:

   • Version 1: One (1) Agent

     • Linux: click here
     • Mac OSX: click here
     • Windows (32-bit): click here
     • Windows (64-bit): click here

   • Version 2: Twenty (20) Agents

     • 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.

   (For AWS) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link (version 1) or this link (version 2) to obtain the "headless" version of the environment. You will not be able to watch the agent without enabling a virtual screen, but you will be able to train the agent. (To watch the agent, you should follow the instructions to enable a virtual screen, and then download the environment for the Linux operating system above.)

6. Place the file in the GitHub repository, in the Continous-Control/ folder, and unzip (or decompress) the file.

Instructions

Follow the instructions in Continuous_Control.ipynb to get started with training the agent!