Udacity Deep Reinforcement Learning Nanodegree, third project "Collaboration and Competition".

The learning algorithm to solve the task was a DDPG controlling both players and trained via self-play and shared replay buffer, for details see the report.

In this project the Tennis environment is solved. The goal of both agents playing against each other is to keep the ball in play.

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.

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).

The observation space consists of 24 dimensions 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.

.
├── checkpoint_actor_64x64.pth            # stored weights for trained actor network (2 hidden 64 unit layers)
├── checkpoint_actor.pth                  # latest stored weights for trained actor network
├── checkpoint_critic_64x64.pth           # stored weights for trained critic network (2 hidden 64 unit layers)
├── checkpoint_critic.pth                 # latest stored weights for trained critic network
├── ddpg_agent.py                         # agent to interact and learn from environment
├── model.py                              # neural network model (in Pytorch)
├── Report.pdf                            # report of the implementation and details of the learning algorithm
├── Tennis.ipynb                          # main code for training and testing the agent
└── README.md

• collections
• copy
• matplotlib
• numpy
• pandas
• random
• torch
• unityagents

Note: The Unity environment did not work on Mac OS for Python version 3.7 and higher. Python version 3.6 worked well.

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 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.)

Follow the instructions in Tennis.ipynb to get started with training your own agent!