Environments to train reinforcement learning agents.
Currently contains one environment, Minimize1DSimple, minimizing negative log-likelihood fit to a gaussian sample. The environment takes a couple of configuration arguments and should therefore be instantiated from the class. It adheres to the openai gym API and can also be instantiated with `gym.make("minimize-1d-simple-v0").
Example:
from phynix_gym import Minimize1DSimple
env = Minimize1DSimple()
nll, mu, mu_grad, sigma, sigma_grad = env.reset() # return state
state, reward, done, info = env.step([0.1, 0.3]) # corresponds to [mu, sigma]
env.render() # shows a nll contour plot, the position, gradient and the minimum