For this project, you 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.
- clone and set up environment by running
git clone https://github.com/thanakijwanavit/deep_rl_env.git
- install docker according to the instruction here
- initialize environment by running
bash ./deep_rl_env/start_script.sh
--> this will create a docker container which can be accessed throughbash ./deep_rl_env/monitor_script.sh
- within the docker container, run
git clone [email protected]:thanakijwanavit/tennis_maddpg_collaboration_competition.git
- download the necessary environment from this link
- extract zip file
unzip Tennis_Linux.zip
Follow the instructions in Tennis.ipynb
to get started with training your own agent!
After you have successfully completed the project, you might like to solve the more difficult Soccer environment.
In this environment, the goal is to train a team of agents to play soccer.
You can read more about this environment in the ML-Agents GitHub here. To solve this harder task, you'll need to download a new Unity environment. (Note: Udacity students should not submit a project with this new environment.)
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
Then, place the file in the p3_collab-compet/
folder in the DRLND GitHub repository, and unzip (or decompress) the file. Next, open Soccer.ipynb
and follow the instructions to learn how to use the Python API to control the agent.
(For AWS) If you'd like to train the agents 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 agents without enabling a virtual screen, but you will be able to train the agents. (To watch the agents, you should follow the instructions to enable a virtual screen, and then download the environment for the Linux operating system above.)