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)]