TennisAgent-PyTorch

This model is developed as a solution to Project 3 of Udacity Deep Reinforcement Learning Nanodegree

^{Image from official repo}

Installation

Install the package requirements for this repository

pip install -r requirements.txt

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.

Getting the environment

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

Training the agent

For training the agent on the single-agent version of the environment, the model can be run by using one of the following (only tested on windows!):

python main.py --environment env_unity/Tennis/Tennis.exe --name tennis_per --memory per
python main.py --environment env_unity/Tennis/Tennis.exe --name tennis_replay

Testing the agent

Once the agent has been trained, it can be run as follows:

python main.py --environment env_unity/Tennis/Tennis.exe --name tennis_replay --test
python main.py --environment env_unity/Tennis/Tennis.exe --name tennis_per --test