from rllab.envs.base import Env
from rllab.spaces import Box
from rllab.envs.base import Step
import numpy as np


class PointEnv(Env):
    @property
    def observation_space(self):
        return Box(low=-np.inf, high=np.inf, shape=(2,))

    @property
    def action_space(self):
        return Box(low=-0.1, high=0.1, shape=(2,))

    def reset(self, **kwargs):
        self._state = np.random.uniform(-1, 1, size=(2,))
        observation = np.copy(self._state)
        return observation

    def step(self, action):
        self._state = self._state + action
        x, y = self._state
        reward = - (x ** 2 + y ** 2) ** 0.5
        done = abs(x) < 0.01 and abs(y) < 0.01
        next_observation = np.copy(self._state)
        return Step(observation=next_observation, reward=reward, done=done)

    def render(self):
        print('current state:', self._state)
