Repository for the Udacity RL Specialization first project with Deep Q Learning.
This project has the objective to train an Agent using Deep Q Networks.
The environment consists in a square world with yellow and blue bananas. The agent has the objective to collect as many yellow bananas as possible while avoiding the blue ones. The agent has 4 possible actions: move forward, move backward, turn left and turn right. The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around the agent's forward direction. A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana. The task is episodic, and in order to solve the environment, the agent must get an average score of +13 over 100 consecutive episodes.
To install the env, select the environment that matches your operating system:
The Github Repo already contains the environment for MacOS. If you are using another OS, you must download the environment and place it in the folder rl-dqn-collect-bananas/.
To train the agent you must open the notebook Navigation.ipynb and run all the cells. The agent will be trained and the weights will be saved in the file model.pth.
To visualize the trained agent you must open the notebook Play.ipynb and run all the cells.
The dependencies are listed in the file requirements.txt in the folder python/. To install them you can run the following command:
cd python
pip install .ps: This command is in the first cell of the notebook. You should run it just once.
It is highly recommended to use a virtual environment to install the dependencies. you can do this by running the following commands:
- Linux or Mac:
```bash
conda create --name drlnd python=3.6
source activate drlnd
```
- Windows:
```bash
conda create --name drlnd python=3.6
activate drlnd
```