For this project, you will 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. Thus, the goal of your 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.
The task is episodic, and in order to solve the environment, your agent must get an average score of +13 over 100 consecutive episodes.
model.py - PyTorch model of the Q Network
agent.py - DQN agent
Navigation.ipynb - Jupyter notebook with training and visualization code
checkpoint.pth - Final saved PyTorch checkpoint
Install dependencies by running pip install -r requirements.txt.
Run or modify the learning process with jupyter notebook -> Navigation.ipynb.
Enabling variants:
- Double DQN - set
doubleQ=Truewhen initializingAgent - Dueling DQN - set
dueling=Truewhen initializingAgent - Prioritized Experience Replay - set
per=Truewhen initializingAgent