def test_can_interact_with_attention_env(self):
env = attention_allocation.LocationAllocationEnv()
agent = allocation_agents.MLEGreedyAgent(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=rewards.VectorSumReward('incidents_seen'))
test_util.run_test_simulation(env=env, agent=agent)
def test_can_interact_with_attention_env(self):
env = attention_allocation.LocationAllocationEnv()
agent = allocation_agents.MLEGreedyAgent(
action_space=env.action_space,
observation_space=env.observation_space,
reward_fn=None)
test_util.run_test_simulation(env=env, agent=agent)
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])