ml-agents/ml-agents/mlagents/trainers/tests/torch/test_hybrid.py

import attr
import pytest


from mlagents.trainers.tests.simple_test_envs import (
    SimpleEnvironment,
    MemoryEnvironment,
)

from mlagents.trainers.settings import NetworkSettings

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

BRAIN_NAME = "1D"

PPO_TORCH_CONFIG = ppo_dummy_config()
SAC_TORCH_CONFIG = sac_dummy_config()


@pytest.mark.check_environment_trains
@pytest.mark.parametrize("action_size", [(1, 1), (2, 2), (1, 2), (2, 1)])
def test_hybrid_ppo(action_size):
    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_size, step_size=0.8)
    new_network_settings = attr.evolve(PPO_TORCH_CONFIG.network_settings)
    new_hyperparams = attr.evolve(
        PPO_TORCH_CONFIG.hyperparameters,
        batch_size=64,
        buffer_size=1024,
        learning_rate=1e-3,
    )
    config = attr.evolve(
        PPO_TORCH_CONFIG,
        hyperparameters=new_hyperparams,
        network_settings=new_network_settings,
        max_steps=10000,
    )
    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)


@pytest.mark.check_environment_trains
@pytest.mark.parametrize("num_visual", [1, 2])
def test_hybrid_visual_ppo(num_visual):
    env = SimpleEnvironment(
        [BRAIN_NAME], num_visual=num_visual, num_vector=0, action_sizes=(1, 1)
    )
    new_hyperparams = attr.evolve(
        PPO_TORCH_CONFIG.hyperparameters, learning_rate=3.0e-4
    )
    config = attr.evolve(PPO_TORCH_CONFIG, hyperparameters=new_hyperparams)
    check_environment_trains(env, {BRAIN_NAME: config}, training_seed=1336)


@pytest.mark.check_environment_trains
def test_hybrid_recurrent_ppo():
    env = MemoryEnvironment([BRAIN_NAME], action_sizes=(1, 1), step_size=0.5)
    new_network_settings = attr.evolve(
        PPO_TORCH_CONFIG.network_settings,
        memory=NetworkSettings.MemorySettings(memory_size=16),
    )
    new_hyperparams = attr.evolve(
        PPO_TORCH_CONFIG.hyperparameters,
        learning_rate=1.0e-3,
        batch_size=64,
        buffer_size=512,
    )
    config = attr.evolve(
        PPO_TORCH_CONFIG,
        hyperparameters=new_hyperparams,
        network_settings=new_network_settings,
        max_steps=5000,
    )
    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)


@pytest.mark.check_environment_trains
@pytest.mark.parametrize("action_size", [(1, 1), (2, 2), (1, 2), (2, 1)])
def test_hybrid_sac(action_size):
    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_size, step_size=0.8)

    new_hyperparams = attr.evolve(
        SAC_TORCH_CONFIG.hyperparameters,
        buffer_size=50000,
        batch_size=256,
        buffer_init_steps=0,
    )
    config = attr.evolve(
        SAC_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=2200
    )
    check_environment_trains(
        env, {BRAIN_NAME: config}, success_threshold=0.9, training_seed=1336
    )


@pytest.mark.check_environment_trains
@pytest.mark.parametrize("num_visual", [1, 2])
def test_hybrid_visual_sac(num_visual):
    env = SimpleEnvironment(
        [BRAIN_NAME], num_visual=num_visual, num_vector=0, action_sizes=(1, 1)
    )
    new_hyperparams = attr.evolve(
        SAC_TORCH_CONFIG.hyperparameters,
        buffer_size=50000,
        batch_size=128,
        learning_rate=3.0e-4,
    )
    config = attr.evolve(
        SAC_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=3000
    )
    check_environment_trains(env, {BRAIN_NAME: config})


@pytest.mark.check_environment_trains
def test_hybrid_recurrent_sac():
    env = MemoryEnvironment([BRAIN_NAME], action_sizes=(1, 1), step_size=0.5)
    new_networksettings = attr.evolve(
        SAC_TORCH_CONFIG.network_settings,
        memory=NetworkSettings.MemorySettings(memory_size=16, sequence_length=16),
    )
    new_hyperparams = attr.evolve(
        SAC_TORCH_CONFIG.hyperparameters,
        batch_size=256,
        learning_rate=1e-3,
        buffer_init_steps=1000,
        steps_per_update=2,
    )
    config = attr.evolve(
        SAC_TORCH_CONFIG,
        hyperparameters=new_hyperparams,
        network_settings=new_networksettings,
        max_steps=3500,
    )
    check_environment_trains(env, {BRAIN_NAME: config}, training_seed=1212)