An comparison of multiagent training techniques for building deep reinforcement learning agents for the Unity 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. Note that this reward structure creates a cooperative game as the agents are rewarded for a prolonged rally.
To set up your python environment to run the code in this repository, follow the instructions below.
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
Follow the instructions in this repository https://github.com/openai/gym to perform a minimal install of OpenAI gym.
Clone this repository (if you haven't already!) then, install several dependencies.
pip install -r requirements.txt
Create an IPython kernel for the drlnd environment.
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.
You will also need to download the environment file and unzip it in the project root directory. The environments are (by platform)
The implementation approach is based on DDPG and SAC. The code provides a number of approaches to multi agent training: independent, self play and shared critic which are compared.
In order to use the project open the Jupyter notebook Report.ipynb. This notebook contains further details of the environment, agents and training.