def test_qconfig_values(self):
params = [
"agent.optimizer.learning_rate=0.01",
"agent.fc_layer_params=[100, 150, 90]",
]
conf = agent_application.make_config(QConfig(), params)
self.assertEqual(conf.agent.optimizer.learning_rate, 0.01)
# default is Adam
self.assertEqual(conf.agent.optimizer.optimizer, Optimizer.Adam)
self.assertEqual(conf.agent.fc_layer_params, [100, 150, 90])
def test_standard_config_dqn(self, mock_qnetwork, mock_agent):
params = [
"agent.optimizer.learning_rate=0.01",
"policy.epsilon_greedy=0.01",
"trajectory.n_step=1",
"agent.boltzmann_temperature=200",
"agent.emit_log_probability=True",
"agent.target_update_tau=1.0",
"agent.target_update_period=2",
"agent.gamma=1.1",
"agent.reward_scale_factor=1.2",
"agent.gradient_clipping=1.5",
"agent.debug_summaries=True",
"agent.summarize_grads_and_vars=False",
"agent.name=Patrick",
"agent.fc_layer_params=[100, 150, 90]",
]
dataspec = agent_application.DataSpec(
observation_spec=specs.ArraySpec([1, 2, 3], int),
action_spec=specs.ArraySpec([1], float))
conf = agent_application.make_config(QConfig(), params)
agent_trainer = DQNAgent(dataspec, conf)
agent = agent_trainer.init_agent()
mock_qnetwork.assert_called_once_with(dataspec.observation_spec,
dataspec.action_spec,
fc_layer_params=[100, 150, 90])
mock_agent.assert_called_once_with(
time_step_spec=mock.ANY, # TODO
action_spec=dataspec.action_spec,
q_network=mock_qnetwork.return_value,
train_step_counter=mock.ANY, # TODO
optimizer=mock.ANY, #TODO
epsilon_greedy=0.01,
n_step_update=1,
boltzmann_temperature=200,
emit_log_probability=True,
target_update_tau=1.0,
target_update_period=2,
gamma=1.1,
reward_scale_factor=1.2,
gradient_clipping=1.5,
debug_summaries=True,
summarize_grads_and_vars=False,
name="Patrick",
)
self.assertEqual(agent, mock_agent.return_value)
def test_ignore_missing_config_dqn(self, mock_qnetwork, mock_agent):
params = ["agent.fc_layer_params=[100, 150, 90]"]
dataspec = agent_application.DataSpec(
observation_spec=specs.ArraySpec([1, 2, 3], int),
action_spec=specs.ArraySpec([1], float))
conf = agent_application.make_config(QConfig(), params)
agent_trainer = DQNAgent(dataspec, conf)
agent = agent_trainer.init_agent()
mock_qnetwork.assert_called_once_with(dataspec.observation_spec,
dataspec.action_spec,
fc_layer_params=[100, 150, 90])
mock_agent.assert_called_once_with(
time_step_spec=mock.ANY, # TODO
action_spec=dataspec.action_spec,
q_network=mock_qnetwork.return_value,
train_step_counter=mock.ANY, # TODO
optimizer=mock.ANY, #TODO
epsilon_greedy=conf.policy.epsilon_greedy,
n_step_update=conf.trajectory.n_step)
self.assertEqual(agent, mock_agent.return_value)