import numpy as np import gym import time from gym.spaces import Box, Discrete, MultiDiscrete, MultiBinary, Tuple, Dict spaces = [ Box(low=np.array(-1.), high=np.array(1.), dtype=np.float64), Box(low=np.array([0.]), high=np.array([10.]), dtype=np.float32), Box(low=np.array([-1., 0., 0.]), high=np.array([1., 1., 1.]), dtype=np.float32), Box(low=np.array([[-1., 0.], [0., -1.]]), high=np.ones((2, 2)), dtype=np.float32), Box(low=0, high=255, shape=(), dtype=np.uint8), Box(low=0, high=255, shape=(32, 32, 3), dtype=np.uint8), Discrete(2), Tuple((Discrete(3), Discrete(5))), Tuple((Discrete(7), Box(low=np.array([0., -1.]), high=np.array([1., 1.]), dtype=np.float32))), MultiDiscrete([11, 13, 17]), MultiBinary(19), Dict({ 'position': Discrete(23), 'velocity': Box(low=np.array([0.]), high=np.array([1.]), dtype=np.float32) }), Dict({ 'position': Dict({'x': Discrete(29), 'y': Discrete(31)}), 'velocity': Tuple((Discrete(37), Box(low=0, high=255, shape=(), dtype=np.uint8))) }) ] HEIGHT, WIDTH = 64, 64 class UnittestSlowEnv(gym.Env): def __init__(self, slow_reset=0.3): super(UnittestSlowEnv, self).__init__() self.slow_reset = slow_reset self.observation_space = Box(low=0, high=255, shape=(HEIGHT, WIDTH, 3), dtype=np.uint8) self.action_space = Box(low=0., high=1., shape=(), dtype=np.float32) def reset(self): if self.slow_reset > 0: time.sleep(self.slow_reset) return self.observation_space.sample() def step(self, action): time.sleep(action) observation = self.observation_space.sample() reward, done = 0., False return observation, reward, done, {} def make_env(env_name, seed): def _make(): env = gym.make(env_name) env.seed(seed) return env return _make def make_slow_env(slow_reset, seed): def _make(): env = UnittestSlowEnv(slow_reset=slow_reset) env.seed(seed) return env return _make