def select_env(model_class: BaseAlgorithm) -> gym.Env:
"""
Selects an environment with the correct action space as DQN only supports discrete action space
"""
if model_class == DQN:
return IdentityEnv(10)
else:
return IdentityEnvBox(10)
def select_env(model_class: BaseAlgorithm) -> gym.Env:
"""
Selects an environment with the correct action space as QRDQN, DQNClipped, DQNReg only support discrete action space
"""
if model_class in {QRDQN, DQNReg, DQNClipped}:
return IdentityEnv(10)
else:
return IdentityEnvBox(10)
def test_identity(model_name):
"""
Test if the algorithm (with a given policy)
can learn an identity transformation (i.e. return observation as an action)
:param model_name: (str) Name of the RL model
"""
env = DummyVecEnv([lambda: IdentityEnv(10)])
model = LEARN_FUNC_DICT[model_name](env)
evaluate_policy(model, env, n_eval_episodes=20, reward_threshold=90)
obs = env.reset()
assert model.action_probability(obs).shape == (
1, 10), "Error: action_probability not returning correct shape"
action = env.action_space.sample()
action_prob = model.action_probability(obs, actions=action)
assert np.prod(action_prob.shape) == 1, "Error: not scalar probability"
action_logprob = model.action_probability(obs, actions=action, logp=True)
assert np.allclose(action_prob,
np.exp(action_logprob)), (action_prob, action_logprob)
# Free memory
del model, env
from stable_baselines3 import A2C, PPO, SAC, TD3, DQN
from stable_baselines3.common.identity_env import (IdentityEnvBox, IdentityEnv,
IdentityEnvMultiBinary,
IdentityEnvMultiDiscrete)
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.noise import NormalActionNoise
DIM = 4
@pytest.mark.parametrize("model_class", [A2C, PPO, DQN])
@pytest.mark.parametrize("env", [
IdentityEnv(DIM),
IdentityEnvMultiDiscrete(DIM),
IdentityEnvMultiBinary(DIM)
])
def test_discrete(model_class, env):
env_ = DummyVecEnv([lambda: env])
kwargs = {}
n_steps = 3000
if model_class == DQN:
kwargs = dict(learning_starts=0)
n_steps = 4000
# DQN only support discrete actions
if isinstance(env, (IdentityEnvMultiDiscrete, IdentityEnvMultiBinary)):
return
model = model_class('MlpPolicy', env_, gamma=0.5, seed=1,
import numpy as np
import pytest
from stable_baselines3 import A2C, DDPG, DQN, PPO, SAC, TD3
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.identity_env import IdentityEnv, IdentityEnvBox, IdentityEnvMultiBinary, IdentityEnvMultiDiscrete
from stable_baselines3.common.noise import NormalActionNoise
from stable_baselines3.common.vec_env import DummyVecEnv
DIM = 4
@pytest.mark.parametrize("model_class", [A2C, PPO, DQN])
@pytest.mark.parametrize("env", [IdentityEnv(DIM), IdentityEnvMultiDiscrete(DIM), IdentityEnvMultiBinary(DIM)])
def test_discrete(model_class, env):
env_ = DummyVecEnv([lambda: env])
kwargs = {}
n_steps = 3000
if model_class == DQN:
kwargs = dict(learning_starts=0)
n_steps = 4000
# DQN only support discrete actions
if isinstance(env, (IdentityEnvMultiDiscrete, IdentityEnvMultiBinary)):
return
model = model_class("MlpPolicy", env_, gamma=0.5, seed=1, **kwargs).learn(n_steps)
evaluate_policy(model, env_, n_eval_episodes=20, reward_threshold=90)
obs = env.reset()
assert np.shape(model.predict(obs)[0]) == np.shape(obs)
def test_discrete(model_class):
env = IdentityEnv(10)
model = model_class('MlpPolicy', env, gamma=0.5, seed=0).learn(3000)
evaluate_policy(model, env, n_eval_episodes=20, reward_threshold=90)
