File size: 2,323 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 | import gym
import numpy as np
import pytest
from stable_baselines3 import A2C, DDPG, DQN, PPO, SAC, TD3
from stable_baselines3.common.evaluation import evaluate_policy
class DummyMultiDiscreteSpace(gym.Env):
def __init__(self, nvec):
super(DummyMultiDiscreteSpace, self).__init__()
self.observation_space = gym.spaces.MultiDiscrete(nvec)
self.action_space = gym.spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32)
def reset(self):
return self.observation_space.sample()
def step(self, action):
return self.observation_space.sample(), 0.0, False, {}
class DummyMultiBinary(gym.Env):
def __init__(self, n):
super(DummyMultiBinary, self).__init__()
self.observation_space = gym.spaces.MultiBinary(n)
self.action_space = gym.spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32)
def reset(self):
return self.observation_space.sample()
def step(self, action):
return self.observation_space.sample(), 0.0, False, {}
@pytest.mark.parametrize("model_class", [SAC, TD3, DQN])
@pytest.mark.parametrize("env", [DummyMultiDiscreteSpace([4, 3]), DummyMultiBinary(8)])
def test_identity_spaces(model_class, env):
"""
Additional tests for DQ/SAC/TD3 to check observation space support
for MultiDiscrete and MultiBinary.
"""
# DQN only support discrete actions
if model_class == DQN:
env.action_space = gym.spaces.Discrete(4)
env = gym.wrappers.TimeLimit(env, max_episode_steps=100)
model = model_class("MlpPolicy", env, gamma=0.5, seed=1, policy_kwargs=dict(net_arch=[64]))
model.learn(total_timesteps=500)
evaluate_policy(model, env, n_eval_episodes=5, warn=False)
@pytest.mark.parametrize("model_class", [A2C, DDPG, DQN, PPO, SAC, TD3])
@pytest.mark.parametrize("env", ["Pendulum-v0", "CartPole-v1"])
def test_action_spaces(model_class, env):
if model_class in [SAC, DDPG, TD3]:
supported_action_space = env == "Pendulum-v0"
elif model_class == DQN:
supported_action_space = env == "CartPole-v1"
elif model_class in [A2C, PPO]:
supported_action_space = True
if supported_action_space:
model_class("MlpPolicy", env)
else:
with pytest.raises(AssertionError):
model_class("MlpPolicy", env)
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