For this project, you will train an agent to navigate (and collect bananas!) in a large, square world.
In this environment, a double-jointed arm can move to target locations. A reward of +0.1 is provided for each step that the agent's hand is in the goal location. Thus, the goal of your agent is to maintain its position at the target location for as many time steps as possible.
The observation space consists of 33 variables corresponding to position, rotation, velocity, and angular velocities of the arm. Each action is a vector with four numbers, corresponding to torque applicable to two joints. Every entry in the action vector should be a number between -1 and 1.
The barrier for solving the second version of the environment is slightly different, to take into account the presence of many agents. In particular, your agents must get an average score of +30 (over 100 consecutive episodes, and over all 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 20 (potentially different) scores. We then take the average of these 20 scores. This yields an average score for each episode (where the average is over all 20 agents). The environment is considered solved, when the average (over 100 episodes) of those average scores is at least +30.
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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 environment.
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Place the file in the root of this repository 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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Once you have the repository cloned, navigate to the
python/folder. Then, install several dependencies.
pip install .python -m ipykernel install --user --name=drlnd- Before running code in a notebook, change the kernel to match the
drlndenvironment by using the drop-downKernelmenu.
To run the notebook use the following command in the root of this repository
jupyter notebook Continuous_Control.ipynbThis will launch the jupyter notebook interface with the Continuous_Control.ipynb. Once this has loaded, run all the cells (Cell > Run all). It may take some time as it will run through all 10 configurations.