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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 | import pytest
from stable_baselines3 import A2C, DQN, PPO, SAC, TD3
from stable_baselines3.common.noise import NormalActionNoise
N_STEPS_TRAINING = 3000
SEED = 0
@pytest.mark.parametrize("algo", [A2C, DQN, PPO, SAC, TD3])
def test_deterministic_training_common(algo):
results = [[], []]
rewards = [[], []]
# Smaller network
kwargs = {"policy_kwargs": dict(net_arch=[64])}
if algo in [TD3, SAC]:
env_id = "Pendulum-v0"
kwargs.update({"action_noise": NormalActionNoise(0.0, 0.1), "learning_starts": 100})
else:
env_id = "CartPole-v1"
if algo == DQN:
kwargs.update({"learning_starts": 100})
for i in range(2):
model = algo("MlpPolicy", env_id, seed=SEED, **kwargs)
model.learn(N_STEPS_TRAINING)
env = model.get_env()
obs = env.reset()
for _ in range(100):
action, _ = model.predict(obs, deterministic=False)
obs, reward, _, _ = env.step(action)
results[i].append(action)
rewards[i].append(reward)
assert sum(results[0]) == sum(results[1]), results
assert sum(rewards[0]) == sum(rewards[1]), rewards
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