DDPG Algorithm to solve Unity Reacher Environment

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.

For this project, we will provide you with two separate versions of the Unity environment:

• The first version contains a single agent.
• The second version contains 20 identical agents, each with its own copy of the environment.

The second version is useful for algorithms like PPO, A3C, and D4PG that use multiple (non-interacting, parallel) copies of the same agent to distribute the task of gathering experience.

Note that your project submission need only solve one of the two versions of the environment.

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

The barrier for solving the second version of the environment is slightly different, to take into account the presence of many agents. In particular, your agents must get an average score of +30 (over 100 consecutive episodes, and over all agents). Specifically,

• 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. In the case of the plot above, the environment was solved at episode 63, since the average of the average scores from episodes 64 to 163 (inclusive) was greater than +30.

The Unity game engine team has developed a programming environment specifically designed to support intelligent agent programming. The unity environment was used to create a playing area and populate it with 20 double-jointed robot arms and a set of target spheres. To execute the code in this project, you will first need to download and install the Unity ML-Agents code library. The following steps were provided by the Udacity team and will result in a complete environment capable of executing this project.

1. Follow the instructions to install Unity ML-Agents.

2. 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.

   If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link to obtain the environment.

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

The main file of this project is Continuous_Control.ipynb. To use a Jupyter notebook, run the following command from the p2_continuous-control/ folder:

jupyter notebook

and open Continuous_Control.ipynb from the list of files.

Once the project notebook has been opened, you can simply click the "Run->" button in the main menu to execute the python program that opens the Unity Reacher playing field and starts to train 20 agents solve this environment.