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.
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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.
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Place the file in the folder where this project is, and unzip (or decompress) the file.
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The algorithm you need to implement is REINFORCE to solve the said task.
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For submission, you need to submit a zip file in '{Roll No.}.zip' format (For example - 15CS30039.zip) containing all the required files.
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A Report must be submitted which should contain - a.Learning Algorithm, b.Network Architecture, c.Results, d.Future Work.
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All cases of plaigarism will be penalized heavily.