amitkverma / udacity-reinforcement-learning-tennis

Project 3: Tennis Collaboration and Competition

Project description

Introduction

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

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.

Dependencies

To set up your python environment to run the code in this repository, please follow the instructions below.

1. Create (and activate) a new environment with Python 3.6.

   • Linux or Mac:

   conda create --name drlnd python=3.6
   source activate drlnd

   • Windows:

   conda create --name drlnd python=3.6 
   activate drlnd

2. Follow the instructions in this repository to perform a minimal install of OpenAI gym.

   • Next, install the classic control environment group by following the instructions here.
   • Then, install the box2d environment group by following the instructions here.

3. Clone the repository (if you haven't already!), and navigate to the python/ folder. Then, install several dependencies.

git clone https://github.com/udacity/deep-reinforcement-learning.git
cd deep-reinforcement-learning/python
pip install .

4. Create an IPython kernel for the drlnd environment.

python -m ipykernel install --user --name drlnd --display-name "drlnd"

5. Before running code in a notebook, change the kernel to match the drlnd environment by using the drop-down Kernel menu.

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
   • 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.

   (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 this folder, unzip (or decompress) the file and then write the correct path in the argument for creating the environment under the notebook Collaboration_And_Competition_MADDPG.ipynb:

env = env = UnityEnvironment(file_name="Tennis.app")

Description

.
├── images                      #  Supporting images
├── checkpoint                  #  Contains the saved models
│   ├──  agent{n}_checkpoint_actor.pth            # Saved model weights for Actor network
│   ├──  agent{n}_checkpoint_critic.pth           # Saved model weights for Critic Network
├── results                    # Contains images of result
│   ├── maddpg_result.png         # Plot Result for the MADDPG model
├── Collaboration_And_Competition_MADDPG.ipynb      # Notebook with solution using MADDPG model

Instructions

Follow the instructions in Collaboration_And_Competition_MADDPG.ipynb to get started with training your own agent! To watch a trained smart agent, Every notebook will have the section Model in action run that section after loading the enviroment. It will load the save model and start playing the game.

Paper implemented

• Multi Agent Deep Deterministic Policy Gradients (MADDPG) [Paper] [Code]

Results

Plot showing the score per episode over all the episodes. The environment was solved in 1918 episodes.