/p3_collab-compet

Primary LanguagePython

Project 3: Collaboration and Competition

Introduction

For this project, you will work with the Tennis environment.

Trained Agent

In this environment, two agents control rackets to bounce a ball over a net. If an agent hits the ball over the net, it receives a reward of +0.1. If an agent lets a ball hit the ground or hits the ball out of bounds, it receives a reward of -0.01. Thus, the goal of each agent is to keep the ball in play.

The observation space consists of 8 variables corresponding to the position and velocity of the ball and racket. Each agent receives its own, local observation. Two continuous actions are available, corresponding to movement toward (or away from) the net, and jumping.

The task is episodic, and in order to solve the environment, your agents must get an average score of +0.5 (over 100 consecutive episodes, after taking the maximum over both agents). Specifically,

  • After each episode, we add up the rewards that each agent received (without discounting), to get a score for each agent. This yields 2 (potentially different) scores. We then take the maximum of these 2 scores.
  • This yields a single score for each episode.

The environment is considered solved, when the average (over 100 episodes) of those scores is at least +0.5.

Getting Started

  1. Download the environment from one of the links below. You need only select the environment that matches your operating system:

    (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 "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, and then download the environment for the Linux operating system above.)

  2. Place the file in the DRLND GitHub repository, in the p3_collab-compet/ folder, and unzip (or decompress) the file.

Python Dependencies

The following instructions are based on having Miniconda installed on your system. If that is not the case, please follow the Miniconda installation instructions.

  1. Create a conda python 3.6 environment using:

    conda create -n p3 python=3.6

  2. Activate the new environement using:

    conda activate p3

  3. Install the Udacity DRLND python dependencies by running:

    pip install ./python

  4. Install project specific dependencies by running: pip install -r requirements.txt

Submission

The submission consists of this README file explaining how to setup project dependencies, the REPORT file, the project source files and the tensorboard logs and saved models.

The following table describes the different source files in this submission.

File Description
agent.py Collaborative MADDPG agent implementation
buffer.py Replay buffer
main.py Main (training) script
networks.py Neural networks for Actor and Critic
OUNoise.py Ornstein-Uhlenbeck noise generator
utilities.py Various utility functions

The log directory contains the tensorboard log files which can be viewed by running the following command and opening a browser window to https://localhost:3000

tensorboard --logdir=./log/ --host=0.0.0.0 --port=3000 &> /dev/null

The model_dir directory contains models saved during training and in particular the model_dir/episode-1748.pt file which is the model that solved the Tennis environement.

The following command then runs the training script. The log and model_dir directories need to be removed to avoid accumulating data with previous runs.

python main.py