ml-agents/ml-agents/mlagents/trainers/tests/tensorflow/test_simple_rl.py

import math
import tempfile
import pytest
import numpy as np
import attr
from typing import Dict

from mlagents.trainers.tests.simple_test_envs import (
    SimpleEnvironment,
    MemoryEnvironment,
    RecordEnvironment,
)
from mlagents.trainers.trainer_controller import TrainerController
from mlagents.trainers.trainer import TrainerFactory
from mlagents.trainers.simple_env_manager import SimpleEnvManager
from mlagents.trainers.demo_loader import write_demo
from mlagents.trainers.stats import StatsReporter, StatsWriter, StatsSummary
from mlagents.trainers.settings import (
    NetworkSettings,
    SelfPlaySettings,
    BehavioralCloningSettings,
    GAILSettings,
    RewardSignalType,
    EncoderType,
    FrameworkType,
)
from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
from mlagents_envs.side_channel.environment_parameters_channel import (
    EnvironmentParametersChannel,
)
from mlagents_envs.communicator_objects.demonstration_meta_pb2 import (
    DemonstrationMetaProto,
)
from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
from mlagents_envs.communicator_objects.space_type_pb2 import discrete, continuous

from mlagents.trainers.tests.dummy_config import ppo_dummy_config, sac_dummy_config

PPO_TF_CONFIG = attr.evolve(ppo_dummy_config(), framework=FrameworkType.TENSORFLOW)
SAC_TF_CONFIG = attr.evolve(sac_dummy_config(), framework=FrameworkType.TENSORFLOW)

BRAIN_NAME = "1D"


# The reward processor is passed as an argument to _check_environment_trains.
# It is applied to the list of all final rewards for each brain individually.
# This is so that we can process all final rewards in different ways for different algorithms.
# Custom reward processors should be built within the test function and passed to _check_environment_trains
# Default is average over the last 5 final rewards
def default_reward_processor(rewards, last_n_rewards=5):
    rewards_to_use = rewards[-last_n_rewards:]
    # For debugging tests
    print(f"Last {last_n_rewards} rewards:", rewards_to_use)
    return np.array(rewards[-last_n_rewards:], dtype=np.float32).mean()


class DebugWriter(StatsWriter):
    """
    Print to stdout so stats can be viewed in pytest
    """

    def __init__(self):
        self._last_reward_summary: Dict[str, float] = {}

    def get_last_rewards(self):
        return self._last_reward_summary

    def write_stats(
        self, category: str, values: Dict[str, StatsSummary], step: int
    ) -> None:
        for val, stats_summary in values.items():
            if val == "Environment/Cumulative Reward":
                print(step, val, stats_summary.mean)
                self._last_reward_summary[category] = stats_summary.mean


def _check_environment_trains(
    env,
    trainer_config,
    reward_processor=default_reward_processor,
    env_parameter_manager=None,
    success_threshold=0.9,
    env_manager=None,
):
    if env_parameter_manager is None:
        env_parameter_manager = EnvironmentParameterManager()
    # Create controller and begin training.
    with tempfile.TemporaryDirectory() as dir:
        run_id = "id"
        seed = 1337
        StatsReporter.writers.clear()  # Clear StatsReporters so we don't write to file
        debug_writer = DebugWriter()
        StatsReporter.add_writer(debug_writer)
        if env_manager is None:
            env_manager = SimpleEnvManager(env, EnvironmentParametersChannel())
        trainer_factory = TrainerFactory(
            trainer_config=trainer_config,
            output_path=dir,
            train_model=True,
            load_model=False,
            seed=seed,
            param_manager=env_parameter_manager,
            multi_gpu=False,
        )

        tc = TrainerController(
            trainer_factory=trainer_factory,
            output_path=dir,
            run_id=run_id,
            param_manager=env_parameter_manager,
            train=True,
            training_seed=seed,
        )

        # Begin training
        tc.start_learning(env_manager)
        if (
            success_threshold is not None
        ):  # For tests where we are just checking setup and not reward
            processed_rewards = [
                reward_processor(rewards) for rewards in env.final_rewards.values()
            ]
            assert all(not math.isnan(reward) for reward in processed_rewards)
            assert all(reward > success_threshold for reward in processed_rewards)


@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
def test_simple_ppo(action_sizes):
    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_sizes)
    config = attr.evolve(PPO_TF_CONFIG, framework=FrameworkType.TENSORFLOW)
    _check_environment_trains(env, {BRAIN_NAME: config})


@pytest.mark.parametrize("action_sizes", [(0, 2), (2, 0)])
def test_2d_ppo(action_sizes):
    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_sizes, step_size=0.8)
    new_hyperparams = attr.evolve(
        PPO_TF_CONFIG.hyperparameters, batch_size=64, buffer_size=640
    )
    config = attr.evolve(
        PPO_TF_CONFIG,
        hyperparameters=new_hyperparams,
        max_steps=10000,
        framework=FrameworkType.TENSORFLOW,
    )
    _check_environment_trains(env, {BRAIN_NAME: config})


@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
@pytest.mark.parametrize("num_visual", [1, 2])
def test_visual_ppo(num_visual, action_sizes):
    env = SimpleEnvironment(
        [BRAIN_NAME],
        action_sizes=action_sizes,
        num_visual=num_visual,
        num_vector=0,
        step_size=0.2,
    )
    new_hyperparams = attr.evolve(PPO_TF_CONFIG.hyperparameters, learning_rate=3.0e-4)
    config = attr.evolve(
        PPO_TF_CONFIG,
        hyperparameters=new_hyperparams,
        framework=FrameworkType.TENSORFLOW,
    )
    _check_environment_trains(env, {BRAIN_NAME: config})


@pytest.mark.parametrize("num_visual", [1, 2])
@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn", "match3"])
def test_visual_advanced_ppo(vis_encode_type, num_visual):
    env = SimpleEnvironment(
        [BRAIN_NAME],
        action_sizes=(0, 1),
        num_visual=num_visual,
        num_vector=0,
        step_size=0.5,
        vis_obs_size=(5, 5, 5) if vis_encode_type == "match3" else (36, 36, 3),
    )
    new_networksettings = attr.evolve(
        SAC_TF_CONFIG.network_settings, vis_encode_type=EncoderType(vis_encode_type)
    )
    new_hyperparams = attr.evolve(PPO_TF_CONFIG.hyperparameters, learning_rate=3.0e-4)
    config = attr.evolve(
        PPO_TF_CONFIG,
        hyperparameters=new_hyperparams,
        network_settings=new_networksettings,
        max_steps=400,
        summary_freq=100,
        framework=FrameworkType.TENSORFLOW,
    )
    # The number of steps is pretty small for these encoders
    _check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.5)


@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
def test_recurrent_ppo(action_sizes):
    env = MemoryEnvironment([BRAIN_NAME], action_sizes=action_sizes)
    new_network_settings = attr.evolve(
        PPO_TF_CONFIG.network_settings,
        memory=NetworkSettings.MemorySettings(memory_size=16),
    )
    new_hyperparams = attr.evolve(
        PPO_TF_CONFIG.hyperparameters,
        learning_rate=1.0e-3,
        batch_size=64,
        buffer_size=128,
    )
    config = attr.evolve(
        PPO_TF_CONFIG,
        hyperparameters=new_hyperparams,
        network_settings=new_network_settings,
        max_steps=5000,
        framework=FrameworkType.TENSORFLOW,
    )
    _check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)


@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
def test_simple_sac(action_sizes):
    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_sizes)
    config = attr.evolve(
        SAC_TF_CONFIG, framework=FrameworkType.TENSORFLOW, max_steps=900
    )
    _check_environment_trains(env, {BRAIN_NAME: config})


@pytest.mark.parametrize("action_sizes", [(0, 2), (2, 0)])
def test_2d_sac(action_sizes):
    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_sizes, step_size=0.8)
    new_hyperparams = attr.evolve(SAC_TF_CONFIG.hyperparameters, buffer_init_steps=2000)
    config = attr.evolve(
        SAC_TF_CONFIG,
        hyperparameters=new_hyperparams,
        max_steps=10000,
        framework=FrameworkType.TENSORFLOW,
    )
    _check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.8)


@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
@pytest.mark.parametrize("num_visual", [1, 2])
def test_visual_sac(num_visual, action_sizes):
    env = SimpleEnvironment(
        [BRAIN_NAME],
        action_sizes=action_sizes,
        num_visual=num_visual,
        num_vector=0,
        step_size=0.2,
    )
    new_hyperparams = attr.evolve(
        SAC_TF_CONFIG.hyperparameters, batch_size=16, learning_rate=3e-4
    )
    config = attr.evolve(
        SAC_TF_CONFIG,
        hyperparameters=new_hyperparams,
        framework=FrameworkType.TENSORFLOW,
    )
    _check_environment_trains(env, {BRAIN_NAME: config})


@pytest.mark.parametrize("num_visual", [1, 2])
@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn", "match3"])
def test_visual_advanced_sac(vis_encode_type, num_visual):
    env = SimpleEnvironment(
        [BRAIN_NAME],
        action_sizes=(0, 1),
        num_visual=num_visual,
        num_vector=0,
        step_size=0.5,
        vis_obs_size=(5, 5, 5) if vis_encode_type == "match3" else (36, 36, 3),
    )
    new_networksettings = attr.evolve(
        SAC_TF_CONFIG.network_settings, vis_encode_type=EncoderType(vis_encode_type)
    )
    new_hyperparams = attr.evolve(
        SAC_TF_CONFIG.hyperparameters,
        batch_size=16,
        learning_rate=3e-4,
        buffer_init_steps=0,
    )
    config = attr.evolve(
        SAC_TF_CONFIG,
        hyperparameters=new_hyperparams,
        network_settings=new_networksettings,
        max_steps=200,
        framework=FrameworkType.TENSORFLOW,
    )
    # The number of steps is pretty small for these encoders
    _check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.5)


@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
def test_recurrent_sac(action_sizes):
    step_size = 0.2 if action_sizes == (0, 1) else 0.5
    env = MemoryEnvironment(
        [BRAIN_NAME], action_sizes=action_sizes, step_size=step_size
    )
    new_networksettings = attr.evolve(
        SAC_TF_CONFIG.network_settings,
        memory=NetworkSettings.MemorySettings(memory_size=16),
    )
    new_hyperparams = attr.evolve(
        SAC_TF_CONFIG.hyperparameters,
        batch_size=128,
        learning_rate=1e-3,
        buffer_init_steps=1000,
        steps_per_update=2,
    )
    config = attr.evolve(
        SAC_TF_CONFIG,
        hyperparameters=new_hyperparams,
        network_settings=new_networksettings,
        max_steps=4000,
        framework=FrameworkType.TENSORFLOW,
    )
    _check_environment_trains(env, {BRAIN_NAME: config})


@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
def test_simple_ghost(action_sizes):
    env = SimpleEnvironment(
        [BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], action_sizes=action_sizes
    )
    self_play_settings = SelfPlaySettings(
        play_against_latest_model_ratio=1.0, save_steps=2000, swap_steps=2000
    )
    config = attr.evolve(
        PPO_TF_CONFIG,
        self_play=self_play_settings,
        max_steps=2500,
        framework=FrameworkType.TENSORFLOW,
    )
    _check_environment_trains(env, {BRAIN_NAME: config})


@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
def test_simple_ghost_fails(action_sizes):
    env = SimpleEnvironment(
        [BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], action_sizes=action_sizes
    )
    # This config should fail because the ghosted policy is never swapped with a competent policy.
    # Swap occurs after max step is reached.
    self_play_settings = SelfPlaySettings(
        play_against_latest_model_ratio=1.0, save_steps=2000, swap_steps=4000
    )
    config = attr.evolve(
        PPO_TF_CONFIG,
        self_play=self_play_settings,
        max_steps=2500,
        framework=FrameworkType.TENSORFLOW,
    )
    _check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=None)
    processed_rewards = [
        default_reward_processor(rewards) for rewards in env.final_rewards.values()
    ]
    success_threshold = 0.9
    assert any(reward > success_threshold for reward in processed_rewards) and any(
        reward < success_threshold for reward in processed_rewards
    )


@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
def test_simple_asymm_ghost(action_sizes):
    # Make opponent for asymmetric case
    brain_name_opp = BRAIN_NAME + "Opp"
    env = SimpleEnvironment(
        [BRAIN_NAME + "?team=0", brain_name_opp + "?team=1"], action_sizes=action_sizes
    )
    self_play_settings = SelfPlaySettings(
        play_against_latest_model_ratio=1.0,
        save_steps=10000,
        swap_steps=10000,
        team_change=400,
    )
    config = attr.evolve(
        PPO_TF_CONFIG,
        self_play=self_play_settings,
        max_steps=4000,
        framework=FrameworkType.TENSORFLOW,
    )
    _check_environment_trains(env, {BRAIN_NAME: config, brain_name_opp: config})


@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
def test_simple_asymm_ghost_fails(action_sizes):
    # Make opponent for asymmetric case
    brain_name_opp = BRAIN_NAME + "Opp"
    env = SimpleEnvironment(
        [BRAIN_NAME + "?team=0", brain_name_opp + "?team=1"], action_sizes=action_sizes
    )
    # This config should fail because the team that us not learning when both have reached
    # max step should be executing the initial, untrained poliy.
    self_play_settings = SelfPlaySettings(
        play_against_latest_model_ratio=0.0,
        save_steps=5000,
        swap_steps=5000,
        team_change=2000,
    )
    config = attr.evolve(
        PPO_TF_CONFIG,
        self_play=self_play_settings,
        max_steps=3000,
        framework=FrameworkType.TENSORFLOW,
    )
    _check_environment_trains(
        env, {BRAIN_NAME: config, brain_name_opp: config}, success_threshold=None
    )
    processed_rewards = [
        default_reward_processor(rewards) for rewards in env.final_rewards.values()
    ]
    success_threshold = 0.9
    assert any(reward > success_threshold for reward in processed_rewards) and any(
        reward < success_threshold for reward in processed_rewards
    )


@pytest.fixture(scope="session")
def simple_record(tmpdir_factory):
    def record_demo(action_sizes, num_visual=0, num_vector=1):
        env = RecordEnvironment(
            [BRAIN_NAME],
            action_sizes=action_sizes,
            num_visual=num_visual,
            num_vector=num_vector,
            n_demos=100,
        )
        # If we want to use true demos, we can solve the env in the usual way
        # Otherwise, we can just call solve to execute the optimal policy
        env.solve()
        continuous_size, discrete_size = action_sizes
        use_discrete = True if discrete_size > 0 else False
        agent_info_protos = env.demonstration_protos[BRAIN_NAME]
        meta_data_proto = DemonstrationMetaProto()
        brain_param_proto = BrainParametersProto(
            vector_action_size_deprecated=[2] if use_discrete else [1],
            vector_action_descriptions_deprecated=[""],
            vector_action_space_type_deprecated=discrete
            if use_discrete
            else continuous,
            brain_name=BRAIN_NAME,
            is_training=True,
        )
        action_type = "Discrete" if use_discrete else "Continuous"
        demo_path_name = "1DTest" + action_type + ".demo"
        demo_path = str(tmpdir_factory.mktemp("tmp_demo").join(demo_path_name))
        write_demo(demo_path, meta_data_proto, brain_param_proto, agent_info_protos)
        return demo_path

    return record_demo


@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
@pytest.mark.parametrize("trainer_config", [PPO_TF_CONFIG, SAC_TF_CONFIG])
def test_gail(simple_record, action_sizes, trainer_config):
    demo_path = simple_record(action_sizes)
    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_sizes, step_size=0.2)
    bc_settings = BehavioralCloningSettings(demo_path=demo_path, steps=1000)
    reward_signals = {
        RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
    }
    config = attr.evolve(
        trainer_config,
        reward_signals=reward_signals,
        behavioral_cloning=bc_settings,
        max_steps=500,
        framework=FrameworkType.TENSORFLOW,
    )
    _check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)


@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
def test_gail_visual_ppo(simple_record, action_sizes):
    demo_path = simple_record(action_sizes, num_visual=1, num_vector=0)
    env = SimpleEnvironment(
        [BRAIN_NAME],
        num_visual=1,
        num_vector=0,
        action_sizes=action_sizes,
        step_size=0.2,
    )
    bc_settings = BehavioralCloningSettings(demo_path=demo_path, steps=1500)
    reward_signals = {
        RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
    }
    hyperparams = attr.evolve(PPO_TF_CONFIG.hyperparameters, learning_rate=3e-4)
    config = attr.evolve(
        PPO_TF_CONFIG,
        reward_signals=reward_signals,
        hyperparameters=hyperparams,
        behavioral_cloning=bc_settings,
        max_steps=1000,
        framework=FrameworkType.TENSORFLOW,
    )
    _check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)


@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
def test_gail_visual_sac(simple_record, action_sizes):
    demo_path = simple_record(action_sizes, num_visual=1, num_vector=0)
    env = SimpleEnvironment(
        [BRAIN_NAME],
        num_visual=1,
        num_vector=0,
        action_sizes=action_sizes,
        step_size=0.2,
    )
    bc_settings = BehavioralCloningSettings(demo_path=demo_path, steps=1000)
    reward_signals = {
        RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
    }
    hyperparams = attr.evolve(
        SAC_TF_CONFIG.hyperparameters, learning_rate=3e-4, batch_size=16
    )
    config = attr.evolve(
        SAC_TF_CONFIG,
        reward_signals=reward_signals,
        hyperparameters=hyperparams,
        behavioral_cloning=bc_settings,
        max_steps=500,
        framework=FrameworkType.TENSORFLOW,
    )
    _check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)