File size: 2,204 Bytes
a89d35f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | 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]
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