Udacity Deep Reinforcement Learning Nanodegree
Train an agent to navigate and collect bananas in a large, square world.
A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana. The goal is to collect as many yellow bananas as possible while avoiding blue bananas.
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.
Four discrete actions are available, corresponding to:
0- move forward1- move backward2- turn left3- turn right
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 set up your python environment to run the code in this repository, follow the instructions below.
conda create --name drlnd python=3.6
source activate drlnd
git clone https://github.com/udacity/deep-reinforcement-learning.git
cd deep-reinforcement-learning/python
pip install .
Clone this repo, then run jupyter notebook to access the notebook.