def test_MLEGreedyAgent_works(self):
experiment = _setup_experiment()
experiment.agent_class = allocation_agents.MLEGreedyAgent
experiment.agent_params = allocation_agents.MLEGreedyAgentParams(
burn_steps=5, window=10, alpha=5.0)
result = attention_allocation_experiment.run(experiment)
# Tests that the result is a valid json string.
result = json.loads(result)
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 mle_greedy_alpha5_agent_resource_all_dynamics():
"""Run experiments on a greedy-epsilon mle agent, epsilon=0.1, across dynamics."""
dynamic_values_to_test = [0.0, 0.01, 0.05, 0.1, 0.15]
experiment = _setup_experiment()
experiment.agent_class = allocation_agents.MLEGreedyAgent
experiment.agent_params = allocation_agents.MLEGreedyAgentParams(
burn_steps=25, window=100, alpha=0.75)
reports_dict = {}
for value in dynamic_values_to_test:
print("Running an experiment...")
experiment.env_params.dynamic_rate = value
json_report = attention_allocation_experiment.run(experiment)
report = json.loads(json_report)
print("\n\nMLE Greedy Fair Agent, 6 attention units, alpha=0.75")
_print_discovered_missed_incidents_report(value, report)
output_filename = "mle_greedy_fair_alpha75_6units_%f.json" % value
with open(os.path.join(FLAGS.output_dir, output_filename), "w") as f:
json.dump(report, f)
reports_dict[value] = json_report
return reports_dict