It's the first project for the Udacity Deep Reinforcement Learning Nanodegree. The goal is to implement model based on Deep Q-Network (DQN) to collect yellow bananas and avoid blue bananas in Unity ML-Agents environment.
The simulation contains a single agent navigating in a large, square environment.
A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana. Thus, the goal of agent 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. Given this information, the agent has to learn how to best select actions. Four discrete actions are available:
0- walk forward1- walk backward2- turn left3- turn right
The task is episodic, and in order to solve the environment, your agent must get an average score of +13 over 100 consecutive episodes.
- Prepare new Conda environment following guidelines on Udacity DRL repo
conda install pytorch=0.4.0 -c pytorch before running pip install in Point 3.
- Download custom Banana environment (Unity ML-Agents env) prepared by Udacity
Linux: click here Mac OSX: click here Windows (32-bit): click here Windows (64-bit): click here
The file needs to placed in the root directory of this repository and unzipped.
Path to the executable has to be provided to UnityEnvironment function in Navigation.ipynb
For example on 64-bit Windows:
env = UnityEnvironment(file_name="./Banana_Windows_x86_64/Banana.exe")- Run the
Navigation.ipynbnotebook using thedrlndkernel to train the DQN agent.
Once trained, the model weights will be saved in the same directory in the file model.pth.
- You can see how trained agent move in environment by running
Watch_Agent.ipynbnotebook.