def simple_test(env_fn, learn_fn, min_reward_fraction, n_trials=N_TRIALS):
np.random.seed(0)
np_random.seed(0)
env = DummyVecEnv([env_fn])
with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(
allow_soft_placement=True)).as_default():
tf.set_random_seed(0)
model = learn_fn(env)
sum_rew = 0
done = True
for i in range(n_trials):
if done:
obs = env.reset()
state = model.initial_state
if state is not None:
a, v, state, _ = model.step(obs, S=state, M=[False])
else:
a, v, _, _ = model.step(obs)
obs, rew, done, _ = env.step(a)
sum_rew += float(rew)
print("Reward in {} trials is {}".format(n_trials, sum_rew))
assert sum_rew > min_reward_fraction * n_trials, \
'sum of rewards {} is less than {} of the total number of trials {}'.format(sum_rew, min_reward_fraction, n_trials)
def test_identity(learn_func):
'''
Test if the algorithm (with a given policy)
can learn an identity transformation (i.e. return observation as an action)
'''
np.random.seed(0)
np_random.seed(0)
random.seed(0)
env = DummyVecEnv([lambda: IdentityEnv(10)])
with tf.Graph().as_default(), tf.Session().as_default():
tf.set_random_seed(0)
model = learn_func(env)
N_TRIALS = 1000
sum_rew = 0
obs = env.reset()
for i in range(N_TRIALS):
obs, rew, done, _ = env.step(model.step(obs)[0])
sum_rew += rew
assert sum_rew > 0.9 * N_TRIALS
def test_identity(learn_func):
'''
Test if the algorithm (with a given policy)
can learn an identity transformation (i.e. return observation as an action)
'''
np.random.seed(0)
np_random.seed(0)
random.seed(0)
env = DummyVecEnv([lambda: IdentityEnv(10)])
with tf.Graph().as_default(), tf.Session().as_default():
tf.set_random_seed(0)
model = learn_func(env)
N_TRIALS = 1000
sum_rew = 0
obs = env.reset()
for i in range(N_TRIALS):
obs, rew, done, _ = env.step(model.step(obs)[0])
sum_rew += rew
assert sum_rew > 0.9 * N_TRIALS
