For this project, an agent is trained to 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.
- Agent Reward Function (independent):
- +0.1 To agent when hitting ball over net.
- -0.1 To agent who let ball hit their ground, or hit ball out of bounds.
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. 2 continuous actions are available, corresponding to movement toward (or away from) the net, and jumping.
- Behavior Parameters:
- Vector Observation space:
- 8 variables corresponding to position and velocity of ball and racket.
- Vector Action space:
- (Continuous) Size of 2, corresponding to movement toward net or away from net, and jumping.
- Vector Observation space:
This environment works with two agents playing during an episode. Both agents have the same objective and this allows us to work with only one Agent model and use both agents interact with the environment to make the agent learn and improve our model.
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.
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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 Windows users) Check out this link if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system.
(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.)
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Place the file in the DRLND GitHub repository, in the
p3_collab-compet/folder, and unzip (or decompress) the file.
To set up your python environment to run the code in this repository, follow the instructions below.
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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
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Install all dependencies from
requirements.txt:
pip install -r requirements.txt- Install Pytorch version 0.4.0 with your correct Cuda version (in my case, I'm using cuda 10.0).
conda install -n drlnd pytorch=0.4.0 cudatoolkit=10.0 -c pytorch4 - Create an IPython kernel for the drlnd environment.
python -m ipykernel install --user --name drlnd --display-name "drlnd"Follow the instructions in Report.ipynb to get started with training the agent!
In episode 941 (and after 30:01 minutes training in a local machine using GPU), the agent achieved the expected result 👍 (moving avg > 0.5).
So let's see what happen to the agent:
| We started with a random agent | After 941 episodes agents were able to play for a long time |
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