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=None
)
observation = env.reset()
with self.assertRaises(core.EpisodeDoneError):
agent.act(observation, done=True)
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=None
)
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_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=None
)
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_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=None,
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_can_interact_with_attention_env(self):
env = attention_allocation.LocationAllocationEnv()
agent = allocation_agents.NaiveProbabilityMatchingAgent(
action_space=env.action_space, observation_space=env.observation_space, reward_fn=None
)
test_util.run_test_simulation(env=env, agent=agent)