For this project, you will work with the Reacher environment. In this project an agent is trained to move its double-jointed arm to targets location.
Reward: A reward of +0.1 is provided for each step that the agent's hand is in the goal location.
Goal: The goal of your agent is to maintain its position at the target location for as many time steps as possible.
Observation Space: 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.
Action Space: Every entry in the action vector should be a number between -1 and 1.
In order to consider the environment has been solved, the agent must get an average score of +30 over 100 consecutive episodes.
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Configure a new conda virtual environment for Python 3.6 with the needed requirements as described in this Udacity repository.
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Install Unity Agents using:
pip install unityagents -
The environment provided with this repository is for Mac OS. If you have another OS than please use one of the following links to donwload the environment for your OS replacing the "Banana.app" in this repository.
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) Сheck 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.)
Clone the repository and unpack the environment file in the project folder.
Follow the instructions in Solution.ipynb to get started with training your own agent!