This project is a part of Udacity Deep Reinforcement Learning Nanodegree course which demonstrates how to apply DQN algorithm to the Unity ML-Agents Banana 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 the 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 agent's forward direction. Given this information, the agent has to learn how to best select actions. Four discrete actions are available, corresponding to:
0- move forward.1- move backward.2- turn left.3- turn right.
In order to solve the environment, the agent must get an average score of +13 over 100 consecutive episodes.
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Download the environment from one of the links below. You need only select the environment that matches your operating system:
- Linux: click here
- Mac OSX: click here
- Windows (32-bit): click here
- Windows (64-bit): click here
(For Linux headless setup) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link to obtain the environment.
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Place the file in the root folder, and unzip (or decompress) the file.
Follow the instructions in Navigation.ipynb to train or run pre-trained model.