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.
For my setup, I have developed in Anaconda environment in a Windows 10 machine.
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Please follow the instructions in https://github.com/udacity/deep-reinforcement-learning#dependencies to install all the dependencies required for this project. In addition to this pytorch 1.4.0 was installed locally instead of pytorch 0.4.0
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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 Windows users) Check out this link if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system.
(For AWS) 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 DRLND GitHub repository, in the
p1_navigation/folder, and unzip (or decompress) the file.
Follow the instructions in Navigation.ipynb to get started with training your own agent!
The dqn_agent.py contains the implementation for the agent network.
The model.py contains the model architecture defined for the Q-network that is used by the dqn agent.