def test_discrete_identity(alg): ''' Test if the algorithm (with an mlp policy) can learn an identity transformation (i.e. return observation as an action) ''' kwargs = learn_kwargs[alg] kwargs.update(common_kwargs) learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs) env_fn = lambda: DiscreteIdentityEnv(10, episode_len=100) simple_test(env_fn, learn_fn, 0.9)
def test_continuous_identity(alg): ''' Test if the algorithm (with an mlp policy) can learn an identity transformation (i.e. return observation as an action) to a required precision ''' kwargs = learn_kwargs[alg] kwargs.update(common_kwargs) learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs) env_fn = lambda: BoxIdentityEnv((1, ), episode_len=100) simple_test(env_fn, learn_fn, -0.1)
def test_mnist(alg): ''' Test if the algorithm can learn to classify MNIST digits. Uses CNN policy. ''' learn_kwargs = learn_args[alg] learn_kwargs.update(common_kwargs) learn = get_learn_function(alg) learn_fn = lambda e: learn(env=e, **learn_kwargs) env_fn = lambda: MnistEnv(seed=0, episode_len=100) simple_test(env_fn, learn_fn, 0.6)
def test_env_after_learn(algo): def make_env(): # acktr requires too much RAM, fails on travis env = gym.make('CartPole-v1' if algo == 'acktr' else 'PongNoFrameskip-v4') return env make_session(make_default=True, graph=tf.Graph()) env = SubprocVecEnv([make_env]) learn = get_learn_function(algo) # Commenting out the following line resolves the issue, though crash happens at env.reset(). learn(network='mlp', env=env, total_timesteps=0, load_path=None, seed=None) env.reset() env.close()
def test_cartpole(alg): ''' Test if the algorithm (with an mlp policy) can learn to balance the cartpole ''' kwargs = common_kwargs.copy() kwargs.update(learn_kwargs[alg]) learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs) def env_fn(): env = gym.make('CartPole-v0') env.seed(0) return env reward_per_episode_test(env_fn, learn_fn, 100)
def test_fixed_sequence(alg, rnn): ''' Test if the algorithm (with a given policy) can learn an identity transformation (i.e. return observation as an action) ''' kwargs = learn_kwargs[alg] kwargs.update(common_kwargs) episode_len = 5 env_fn = lambda: FixedSequenceEnv(10, episode_len=episode_len) learn = lambda e: get_learn_function(alg)( env=e, network=rnn, **kwargs ) simple_test(env_fn, learn, 0.7)
def test_serialization(learn_fn, network_fn): ''' Test if the trained model can be serialized ''' if network_fn.endswith('lstm') and learn_fn in ['acktr', 'trpo_mpi', 'deepq']: # TODO make acktr work with recurrent policies # and test # github issue: https://github.com/openai/baselines/issues/194 return env = DummyVecEnv([lambda: MnistEnv(10, episode_len=100)]) ob = env.reset().copy() learn = get_learn_function(learn_fn) kwargs = {} kwargs.update(network_kwargs[network_fn]) kwargs.update(learn_kwargs[learn_fn]) learn = partial(learn, env=env, network=network_fn, seed=0, **kwargs) with tempfile.TemporaryDirectory() as td: model_path = os.path.join(td, 'serialization_test_model') with tf.Graph().as_default(), make_session().as_default(): model = learn(total_timesteps=100) model.save(model_path) mean1, std1 = _get_action_stats(model, ob) variables_dict1 = _serialize_variables() with tf.Graph().as_default(), make_session().as_default(): model = learn(total_timesteps=0, load_path=model_path) mean2, std2 = _get_action_stats(model, ob) variables_dict2 = _serialize_variables() for k, v in variables_dict1.items(): np.testing.assert_allclose(v, variables_dict2[k], atol=0.01, err_msg='saved and loaded variable {} value mismatch'.format(k)) np.testing.assert_allclose(mean1, mean2, atol=0.5) np.testing.assert_allclose(std1, std2, atol=0.5)