https://github.com/redtachyon/udacity-reacher-ppo
My implementation of the second Deep Reinforcement Learning Nanodegree Project
https://github.com/redtachyon/udacity-reacher-ppo
Last synced: over 1 year ago
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My implementation of the second Deep Reinforcement Learning Nanodegree Project
- Host: GitHub
- URL: https://github.com/redtachyon/udacity-reacher-ppo
- Owner: RedTachyon
- Created: 2020-05-03T14:51:38.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-04-30T21:13:18.000Z (about 5 years ago)
- Last Synced: 2025-01-24T12:13:15.858Z (over 1 year ago)
- Language: Python
- Size: 2.18 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
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README
[//]: # (Image References)
[image1]: https://user-images.githubusercontent.com/10624937/43851024-320ba930-9aff-11e8-8493-ee547c6af349.gif "Trained Agent"
[image2]: https://user-images.githubusercontent.com/10624937/43851646-d899bf20-9b00-11e8-858c-29b5c2c94ccc.png "Crawler"
# Project 2: Continuous Control
### Introduction
For this project, you will work with the [Reacher](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Learning-Environment-Examples.md#reacher) environment.
![Trained Agent][image1]
In this environment, a double-jointed arm can move to target locations. A reward of +0.1 is provided for each step that the agent's hand is in the goal location. Thus, the goal of your agent is to maintain its position at the target location for as many time steps as possible.
The observation space consists of 33 variables corresponding to position, rotation, velocity, and angular velocities of the arm. Each action is a vector with four numbers, corresponding to torque applicable to two joints. Every entry in the action vector should be a number between -1 and 1.
### Solving the Environment
Note that your project submission need only solve one of the two versions of the environment.
#### Option 1: Solve the First Version
The task is episodic, and in order to solve the environment, your agent must get an average score of +30 over 100 consecutive episodes.
### Getting Started
I'm assuming the Python environment is set up according to the specification of the exercise, except PyTorch which should
be at least 1.5.0. You can also use `pip install -r requirements.txt`
1. Download the environment from one of the links below. You need only select the environment that matches your operating system:
- **_Version 1: One (1) Agent_**
- Linux: [click here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P2/Reacher/one_agent/Reacher_Linux.zip)
- Mac OSX: [click here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P2/Reacher/one_agent/Reacher.app.zip)
- Windows (32-bit): [click here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P2/Reacher/one_agent/Reacher_Windows_x86.zip)
- Windows (64-bit): [click here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P2/Reacher/one_agent/Reacher_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/P2/Reacher/one_agent/Reacher_Linux_NoVis.zip) (version 1) or [this link](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P2/Reacher/Reacher_Linux_NoVis.zip) (version 2) to obtain the "headless" version of the environment. You will **not** be able to watch the agent without enabling a virtual screen, but you will be able to train the agent. (_To watch the agent, you should follow the instructions to [enable a virtual screen](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-on-Amazon-Web-Service.md), and then download the environment for the **Linux** operating system above._)
2. Place the file in the DRLND GitHub repository, in the `p2_continuous-control/` folder, and unzip (or decompress) the file.
### Instructions
Follow the instructions in `Continuous_Control.ipynb` to get started with training your own agent!
To use tensorboard (if installed on your system), you can type `tensorboard --logdir ~/drlnd_logs` and navigate to `localhost:6006` in your browser to see the training unfold