def test_continuous_action_space_norm():
env = NormalizedGymEnv('MountainCarContinuous-v0')
env.reset()
env.step([0.1])
env.close()
assert GymEnv.env_action_space_is_discrete(env) is False
assert GymEnv.get_env_action_space_dim(env) == 1
def test_discrete_action_space_norm():
env = NormalizedGymEnv('CartPole-v0')
env.reset()
env.step(1)
env.close()
assert GymEnv.env_action_space_is_discrete(env) is True
assert GymEnv.get_env_action_space_dim(env) == 2
def test_norm_reward():
env = NormalizedGymEnv('MountainCarContinuous-v0', normalize_rewards=True)
env.reset()
[env.step([0.01]) for _ in range(env.spec.timestep_limit + 1)]