https://github.com/mrdvince/unity_navigation
Using a Deep Q Network to train an agent to collect only yellow bananas leaving the blue ones in a unity ml-agents environment
https://github.com/mrdvince/unity_navigation
agent deep-q-network dqn-model episodes python pytorch reinforcement-learning udacity-nanodegree
Last synced: 10 months ago
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Using a Deep Q Network to train an agent to collect only yellow bananas leaving the blue ones in a unity ml-agents environment
- Host: GitHub
- URL: https://github.com/mrdvince/unity_navigation
- Owner: mrdvince
- Created: 2020-05-13T21:43:07.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-05-14T05:11:53.000Z (over 5 years ago)
- Last Synced: 2025-02-03T14:45:55.418Z (11 months ago)
- Topics: agent, deep-q-network, dqn-model, episodes, python, pytorch, reinforcement-learning, udacity-nanodegree
- Language: Jupyter Notebook
- Homepage:
- Size: 120 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
[//]: # (Image References)
[image1]: https://user-images.githubusercontent.com/10624937/42135619-d90f2f28-7d12-11e8-8823-82b970a54d7e.gif "Trained Agent"
# Project: Navigation
### Introduction
For this project, you will train an agent to navigate (and collect bananas!) in a large, square world.
![Trained Agent][image1]
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.
### Getting Started
1. Download the environment 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)
(_For Windows users_) Check out [this link](https://support.microsoft.com/en-us/help/827218/how-to-determine-whether-a-computer-is-running-a-32-bit-version-or-64) 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](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-on-Amazon-Web-Service.md)), then please use [this link](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Linux_NoVis.zip) to obtain the environment.
2. Place the file in the DRLND GitHub repository, in the `p1_navigation/` folder, and unzip (or decompress) the file.
### Instructions
Follow the instructions in `Navigation.ipynb` to get started with training your own agent!
### Files included
- `dqn_agent.py`: code for the agent used in the environment
- `model.py`: code containing the Q-Network used as the function approximator by the agent
- `dqn.pth`: saved model weights for the original DQN model
- `Navigation.ipynb`: notebook containing the implemention of the dqn model
### Results
The environment was solved in **402** episodes and an Average Score of 13.02.