smalvar/ContinuousControl-DeepReinforcementLearning

The second project of Deep Reinforcement Learning Nanodegree

Deep Reinforcement Learning Nanodegree - Project 2: Continuous Control

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

Starting

0. Clone this repo.

1. Setup the python enviroment following next link:

2. Copy the content of the p2_continuous-control/ folder from this repo to the p2_continuous-control/ folder of the udacity/deep-reinforcement-learning repo and replaces or remove existing files.

3. Unzip the Reacher_Linux.zip file that is located under the p2_continuous-control/ folder under the same directory. If you are not using Linux, follow the instructions on the botton of this file.

Instructions

Open a jupyter notebook and open the Continuous_Control.ipynb to train or test the agent.

1. For training from zero run all the cells inside the navigation notebook

2. For testing skip the training section and follow the instructions to load the weights.

Dependencies

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

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

   • Linux or Mac:

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

   • Windows:

   conda create --name drlnd python=3.6 
   activate drlnd

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/udacity/deep-reinforcement-learning.git
cd deep-reinforcement-learning/python
pip install .

4. Create an IPython kernel for the drlnd environment.

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

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

![Kernel][image2]

Download the environment (not Linux users)

You need to select the environment that matches your operating system:

• Linux:

• Mac OSX:

• Windows (32-bit):

• Windows (64-bit):

Dependencies

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

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

   • Linux or Mac:

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

   • Windows:

   conda create --name drlnd python=3.6 
   activate drlnd

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/udacity/deep-reinforcement-learning.git
cd deep-reinforcement-learning/python
pip install .

4. Create an IPython kernel for the drlnd environment.

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

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