It's the third project for the Udacity Deep Reinforcement Learning Nanodegree. The goal is to teach both agents to play tennis (so basically, to bounce ball on opponents side until it hits the floor or exits a tennis court). To do that DDPG: Deep Deterministic Policy Gradient for Multi-Agent scenario is used
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, agents must get an average score of +0.5 (over 100 consecutive episodes, after taking the maximum over both agents). In particular,
- 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.
- Prepare new Conda environment following guidelines on Udacity DRL repo
conda install pytorch=0.4.0 -c pytorch
before running pip install
in Point 3.
- Download custom Tennis environment (Unity ML-Agents env) prepared by Udacity
Linux: click here
Mac OSX: click here
Windows (32-bit): click here
Windows (64-bit): click here
The file needs to placed in the root directory of this repository and unzipped.
Path to the executable has to be provided to UnityEnvironment
function in Tennis.ipynb
For example on 64-bit Windows:
env = UnityEnvironment(file_name="./Tennis_Windows_x86_64/Tennis.exe")
- Run the
Tennis.ipynb
notebook using thedrlnd
kernel to train the DDPG agent.
Once trained, the model's critic and actor weights will be saved in the same directory in the file actor.pth
and critic.pth
.
- You can see how trained agents move in environment by running
Watch_Agent.ipynb
notebook.