Tennis - Multi Agent

This project repository is created to show my solution for Udacity's Deep Reinforcement Learning Nanodegree Project 3: Collaboration and Competition.

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. Actually, 8 variables for each time step, and 3 stacked time steps, so our state space consist of 24 varaibles. 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

• First of all you need to configure a Python 3.6 environment with the needed requirements as described in 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.

• Clone this project and make it accesible in your Python environment
• Then you have to install the Unity environment as described in the below:

  • Download the environment that matches your operating system:

    • Linux: click here
    • Mac OSX: click here
    • Windows (32-bit): click here
    • Windows (64-bit): click here

    Then, place the file in the environments/ folder

Report

Algorithm

The agent is trained by using one of the Actor-Critic methods: Deep Deterministic Policy Gradient (DDPG) algorithm. The deep neural network has following layers:

  Actor
  
    - Fully Connected Layers (input: 48, output: 256)
    - Fully Connected Layers (input: 256, output: 128)
    - Fully Connected Layers (input: 128, output: 8)
    
  Critic
  
    - Fully Connected Layers (input: 64, output: 256)
    - Fully Connected Layers (input: 256, output: 128)
    - Fully Connected Layers (input: 128, output: 1)

Parameters:
 
    - Gamma: 0.99
    - Actor Alpha (Learning Rate): 0.001
    - Critic Alpha (Learning Rate): 0.001
    - Epsilon: 1.0
    - Epsilon Decay: 1e-6
    - Replay Buffer Size: 100000
    - Batch Size: 256
    - Update Every: 4

Results

Future Works

- PPO
- A2C
- A3C
- Train to get better optimum score by using best model according to experiences