Korakot’s answer is not correct. You can indeed render OpenAi Gym in colaboratory, albiet kind of slowly using none other than matplotlib. Heres how: Install xvfb & other dependencies (Thanks to Peter for his comment) !apt-get install x11-utils > /dev/null 2>&1 !pip install pyglet > /dev/null 2>&1 !apt-get install -y xvfb python-opengl > /dev/null 2>&1 … Read more
Yes, it is possible to use OpenAI gym environments for multi-agent games. Although in the OpenAI gym community there is no standardized interface for multi-agent environments, it is easy enough to build an OpenAI gym that supports this. For instance, in OpenAI’s recent work on multi-agent particle environments they make a multi-agent environment that inherits … Read more
Box means that you are dealing with real valued quantities. The first array np.array([-1,0,0] are the lowest accepted values, and the second np.array([+1,+1,+1]) are the highest accepted values. In this case (using the comment) we see that we have 3 available actions: Steering: Real valued in [-1, 1] Gas: Real valued in [0, 1] Brake: … Read more
A while ago I have created a fork with Windows support (devs of original repository do not merge or even comment PRs and issues). It does not require neither MSYS/Cygwin nor CMake or Zlib. To simply install atari-py wheels (binaries) use this command: pip install -f https://github.com/Kojoley/atari-py/releases atari_py If you have any distutils supported compiler … Read more
Got a simple solution working: If on a linux server, open jupyter with $ xvfb-run -s “-screen 0 1400x900x24″ jupyter notebook In Jupyter import matplotlib.pyplot as plt %matplotlib inline from IPython import display After each step def show_state(env, step=0, info=””): plt.figure(3) plt.clf() plt.imshow(env.render(mode=”rgb_array”)) plt.title(“%s | Step: %d %s” % (env._spec.id,step, info)) plt.axis(‘off’) display.clear_output(wait=True) display.display(plt.gcf()) Note: … Read more
See my banana-gym for an extremely small environment. Create new environments See the main page of the repository: https://github.com/openai/gym/blob/master/docs/creating_environments.md The steps are: Create a new repository with a PIP-package structure It should look like this gym-foo/ README.md setup.py gym_foo/ __init__.py envs/ __init__.py foo_env.py foo_extrahard_env.py For the contents of it, follow the link above. Details which … Read more