def test_buffer_filling(self):
env = gym.make('MountainCarContinuous-v0')
env = DummyVecEnv([
lambda: env
]) # The algorithms require a vectorized environment to run
policy_kwargs = {
'layers': [64, 64],
'act_fun': tf.keras.activations.tanh
}
action_space_size = env.action_space.shape[0]
observation_space_size = env.observation_space.shape[0]
policy = SAC_MLP_Networks(action_space_size, observation_space_size,
**policy_kwargs)
size = 5
b = Buffer(size, action_space_size, observation_space_size)
run = Runner(env, policy, b, learning_starts=0)
self.assertFalse(b.can_sample(2))
# for some reason calling into get_a tf function causes clashes with other tests
# therefore eager execution used here
# this should be unnecessary when test is run in isolation
tf.config.experimental_run_functions_eagerly(True)
run.run(2)
tf.config.experimental_run_functions_eagerly(False)
self.assertTrue(b.can_sample(2))
def test_can_sample(self):
# env = gym.make('MountainCarContinuous-v0')
size = 2
action_space_size = 3
observation_space_size = 1
b = Buffer(size, action_space_size, observation_space_size)
self.assertFalse(b.can_sample(1))
self.assertFalse(b.can_sample(2))
b.add(np.ones(observation_space_size), np.ones(action_space_size), 0,
np.ones(observation_space_size), False)
self.assertTrue(b.can_sample(1))
self.assertFalse(b.can_sample(2))
b.add(np.ones(observation_space_size), np.ones(action_space_size), 0,
np.ones(observation_space_size), False)
self.assertTrue(b.can_sample(1))
self.assertTrue(b.can_sample(2))
b.add(np.ones(observation_space_size), np.ones(action_space_size), 0,
np.ones(observation_space_size), False)
self.assertFalse(b.can_sample(3))
def test_buffer_filling(self):
env = gym.make('MountainCarContinuous-v0')
policy_kwargs = {
'layers': [64, 64],
'act_fun': tf.keras.activations.tanh
}
action_space_size = env.action_space.shape[0]
observation_space_size = env.observation_space.shape[0]
policy = MLPPolicy(action_space_size, observation_space_size,
**policy_kwargs)
size = 5
b = Buffer(size, action_space_size, observation_space_size)
run = Runner(env, policy, b)
self.assertFalse(b.can_sample(2))
run.run(2)
self.assertTrue(b.can_sample(2))