harshal-jaiswal Goto Github PK

aed_info5100

Northeastern University

banana_navigation_unity_ml-agents

Project Overview 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 the 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. The Environment Follow the instructions below to explore the environment on your own machine! You will also learn how to use the Python API to control your agent. Step 1: Clone the DRLND Repository If you haven't already, please follow the instructions in the DRLND GitHub repository to set up your Python environment. These instructions can be found in README.md at the root of the repository. https://github.com/udacity/deep-reinforcement-learning#dependencies By following these instructions, you will install PyTorch, the ML-Agents toolkit, and a few more Python packages required to complete the project. Step 2: Download the Unity Environment For this project, you will not need to install Unity - this is because we have already built the environment for you, and you can download it from one of the links below. You need only select the environment that matches your operating system: Linux: click here https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Linux.zip Mac OSX: click here https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana.app.zip Windows (32-bit): click here https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Windows_x86.zip Windows (64-bit): click here https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Windows_x86_64.zip Then, place the file in the p1_navigation/ folder in the DRLND GitHub repository, and unzip (or decompress) the file. Step 3: Explore the Environment After you have followed the instructions above, open Navigation.ipynb (located in the p1_navigation/ folder in the DRLND GitHub repository) and follow the instructions to learn how to use the Python API to control the agent.

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In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations. Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with. While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. The dataset for this project originates from the UCI Machine Learning Repository. The datset was donated by Ron Kohavi and Barry Becker, after being published in the article "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid". You can find the article by Ron Kohavi online. The data we investigate here consists of small changes to the original dataset, such as removing the 'fnlwgt' feature and records with missing or ill-formatted entries.

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