For this project, you will work with the Unity 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.

The version of environment we adopt in this project contains 20 identical agents, each with its own copy of the environment.

The RL agents must get an average score of +30 (over 100 consecutive episodes, and over all agents).

After each episode, we add up the rewards that each agent received (without discounting), to get a score for each agent. This yields 20 (potentially different) scores. We then take the average of these 20 scores. This yields an average score for each episode (where the average is over all 20 agents). The environment is considered solved, when the average (over 100 episodes) of those average scores is at least +30.

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

   • Download link for different OS

     • 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.)

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

   • Linux or Mac:

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

   • Windows:

   conda create --name ddpg python=3.6 
   activate ddpg

3. Install dependencies

   conda install numpy torch collections matplotlib

• Train: The program will do training by default. If training is not needed go with option "--no-training"
• Test: The program will NOT do testing by default. If testing is required, use option "--test"

• Train+Test : python Continuous_Control.py --test
• Test Only : python Continuous_Control.py --test --no-train