Unity Banana Navigation
Project created as 1st project on Udacity Deep Reinforcement Learning nanodegree. The goal of the agent is to gather
yellow bananas while avoiding the
blue ones. Here are Unity details of the environment:
Unity brain name: BananaBrain Number of Visual Observations (per agent): 0 Vector Observation space type: continuous Vector Observation space size (per agent): 37 Number of stacked Vector Observation: 1 Vector Action space type: discrete Vector Action space size (per agent): 4 Vector Action descriptions: , , ,
That means we work with state vector containing 37 continous values and 4 discrete actions representing moves (forward, backward, turn left, turn right). The environment is considered solved when agents reaches average score of 13.0 on 100 consecutive episodes.
Make sure you have
python 3.6 installed and virtual environment of your choosing activated. Unity has to be installed on your system. Run:
to install python dependencies. Then you should be able to run
jupyter notebook and view
model.py contains neural network class used as a Q function and file
dqn_agent.py contains agent code.
navigation.ipynb for further details.