def test_episode_done_raises_error(self):
env = attention_allocation.LocationAllocationEnv()
agent = allocation_agents.NaiveProbabilityMatchingAgent(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=rewards.VectorSumReward('incidents_seen'))
observation = env.reset()
with self.assertRaises(core.EpisodeDoneError):
agent.act(observation, done=True)
def test_update_counts(self):
"""Check that counts are updated correctly given an observation."""
env = attention_allocation.LocationAllocationEnv()
agent_params = allocation_agents.NaiveProbabilityMatchingAgentParams()
agent_params.decay_prob = 0
agent = allocation_agents.NaiveProbabilityMatchingAgent(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=rewards.VectorSumReward('incidents_seen'),
params=agent_params)
counts = [3, 6, 8]
observation = np.array([1, 2, 0])
updated_counts = agent._update_beliefs(observation, counts)
self.assertTrue(np.all(np.equal(updated_counts, [4, 8, 8])))
def test_allocate_beliefs_greedy(self):
env_params = attention_allocation.Params(
n_locations=4,
prior_incident_counts=(10, 10, 10, 10),
n_attention_units=5,
incident_rates=[0, 0, 0, 0])
env = attention_allocation.LocationAllocationEnv(params=env_params)
agent_params = allocation_agents.MLEGreedyAgentParams(epsilon=0.0)
agent = allocation_agents.MLEGreedyAgent(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=rewards.VectorSumReward('incidents_seen'),
params=agent_params)
allocation = agent._allocate(5, [5, 2, 1, 1])
self.assertTrue(np.all(np.equal(allocation, [4, 1, 0, 0])))
def test_allocate_beliefs_fair_unsatisfiable(self):
env_params = attention_allocation.Params(
n_locations=4,
prior_incident_counts=(10, 10, 10, 10),
n_attention_units=5,
incident_rates=[0, 0, 0, 0])
env = attention_allocation.LocationAllocationEnv(params=env_params)
agent_params = allocation_agents.MLEGreedyAgentParams(
epsilon=0.0, alpha=0.25)
agent = allocation_agents.MLEGreedyAgent(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=rewards.VectorSumReward('incidents_seen'),
params=agent_params)
with self.assertRaises(gym.error.InvalidAction):
agent._allocate(5, [5, 2, 1, 1])
def test_allocate_by_counts_zero(self):
"""Check allocations are even when counts are zero."""
env = attention_allocation.LocationAllocationEnv()
agent = allocation_agents.NaiveProbabilityMatchingAgent(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=rewards.VectorSumReward('incidents_seen'))
counts = [0, 0, 0]
n_resource = 15
n_samples = 100
samples = [
agent._allocate(n_resource, counts) for _ in range(n_samples)
]
mean_samples = np.sum(samples, axis=0) / float(n_samples)
expected_mean = n_resource / float(len(counts))
std_dev = np.std(samples)
means_close = [
np.abs(mean - expected_mean) < std_dev for mean in mean_samples
]
self.assertTrue(np.all(means_close))
def test__allocate_by_counts(self):
"""Check allocation proportions match probabilities from counts."""
env = attention_allocation.LocationAllocationEnv()
agent = allocation_agents.NaiveProbabilityMatchingAgent(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=rewards.VectorSumReward('incidents_seen'))
counts = [3, 6, 8]
n_resource = 20
n_samples = 100
samples = [
agent._allocate(n_resource, counts) for _ in range(n_samples)
]
counts_normalized = [(count / float(np.sum(counts)))
for count in counts]
samples_normalized = [(count / float(np.sum(samples)))
for count in np.sum(samples, axis=0)]
self.assertTrue(
np.all(np.isclose(counts_normalized, samples_normalized,
atol=0.05)))
def test_vector_sum_reward(self):
reward = rewards.VectorSumReward('x')
self.assertEqual(reward({'x': [1, 5, 2, 4, 7]}), 19)
