testbed/DLR-RM__stable-baselines3/tests/test_predict.py

import gym
import pytest
import torch as th

from stable_baselines3 import A2C, DQN, PPO, SAC, TD3
from stable_baselines3.common.utils import get_device
from stable_baselines3.common.vec_env import DummyVecEnv

MODEL_LIST = [
    PPO,
    A2C,
    TD3,
    SAC,
    DQN,
]


@pytest.mark.parametrize("model_class", MODEL_LIST)
def test_auto_wrap(model_class):
    # test auto wrapping of env into a VecEnv

    # Use different environment for DQN
    if model_class is DQN:
        env_name = "CartPole-v0"
    else:
        env_name = "Pendulum-v0"
    env = gym.make(env_name)
    eval_env = gym.make(env_name)
    model = model_class("MlpPolicy", env)
    model.learn(100, eval_env=eval_env)


@pytest.mark.parametrize("model_class", MODEL_LIST)
@pytest.mark.parametrize("env_id", ["Pendulum-v0", "CartPole-v1"])
@pytest.mark.parametrize("device", ["cpu", "cuda", "auto"])
def test_predict(model_class, env_id, device):
    if device == "cuda" and not th.cuda.is_available():
        pytest.skip("CUDA not available")

    if env_id == "CartPole-v1":
        if model_class in [SAC, TD3]:
            return
    elif model_class in [DQN]:
        return

    # Test detection of different shapes by the predict method
    model = model_class("MlpPolicy", env_id, device=device)
    # Check that the policy is on the right device
    assert get_device(device).type == model.policy.device.type

    env = gym.make(env_id)
    vec_env = DummyVecEnv([lambda: gym.make(env_id), lambda: gym.make(env_id)])

    obs = env.reset()
    action, _ = model.predict(obs)
    assert action.shape == env.action_space.shape
    assert env.action_space.contains(action)

    vec_env_obs = vec_env.reset()
    action, _ = model.predict(vec_env_obs)
    assert action.shape[0] == vec_env_obs.shape[0]

    # Special case for DQN to check the epsilon greedy exploration
    if model_class == DQN:
        model.exploration_rate = 1.0
        action, _ = model.predict(obs, deterministic=False)
        assert action.shape == env.action_space.shape
        assert env.action_space.contains(action)

        action, _ = model.predict(vec_env_obs, deterministic=False)
        assert action.shape[0] == vec_env_obs.shape[0]