In this project we would implement the MADDPG explained at the paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
For this project, we 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.
To set up the python environment to run the code in this repository, the easiest way is to import the project environment in your conda.
- Linux, Ubuntu 18.04 LTS:
conda env create -f drlnd_environment.ymlIf the above has not worked or is not a feasible solution, follow the instructions below.
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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
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Follow the instructions in this repository to perform a minimal install of OpenAI gym.
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Clone this repository, 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 .- If you are going to use Jupyter Notebook, create an IPython kernel for the
drlndenvironment.
python -m ipykernel install --user --name drlnd --display-name "drlnd"Before running code in a notebook, change the kernel to match the drlnd environment by using the drop-down Kernel menu.
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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.)
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Place the file in the DRLND GitHub repository, in the
p3_collab-compet/folder, and unzip (or decompress) the file.
Follow the instructions in Tennis.ipynb to get started with training your own agent!
For more information about the project itself, I encourage you to read the DRLND_P3_Report.pdf. If you still have more questions, do not hesitate and contact me.
The result of one of the succesfull test its showed at tennis_evaluation_agents.mkv. Donwload and show the agents perform!