def _make_app(self, rp_buffer):
params = [
"training.batch_size=32", "training.num_iterations=1",
"project.tensorboard_path=/tmp/test_tb_path"
]
conf = agent_application.make_config(QConfig(), params)
dataspec = agent_application.DataSpec(observation_spec=None,
action_spec=None)
class Env:
def __init__(self):
self.env_id_cols = ["env_id_1"]
self.ts_id_col = "ts_1"
self.obs_cols = ["obs_1", "obs_2"]
app = agent_application.AgentApplication(
data_spec=dataspec,
agent=DQNAgent,
config=conf,
env=Env(),
first_timestep_dt=datetime.datetime.now(),
training_interval=datetime.timedelta(days=1))
app.init_replay_buffer = lambda: rp_buffer
return app
def test_default_config(self, mock_critic_network, mock_actor_network, mock_agent):
params = ["agent.actor_fc_layer_params=[100,10]", "agent.observation_fc_layer_params=[1,2,3]",
"agent.action_fc_layer_params=[1,2,3,4]", "agent.joint_fc_layer_params=[5]"]
obs_spec = "obs_spec"
dataspec = agent_application.DataSpec(
observation_spec=specs.ArraySpec([1,2,3], int),
action_spec=specs.ArraySpec([1], float)
)
conf = agent_application.make_config(DDPGConfig(), params)
agent_trainer = DDPGAgent(dataspec, conf)
agent = agent_trainer.init_agent()
mock_critic_network.assert_called_once_with((dataspec.observation_spec, dataspec.action_spec),
observation_fc_layer_params=[1,2,3],
action_fc_layer_params=[1,2,3,4],
joint_fc_layer_params=[5])
mock_actor_network.assert_called_once_with(dataspec.observation_spec, dataspec.action_spec,
fc_layer_params=[100,10])
mock_agent.assert_called_once_with(
time_step_spec=mock.ANY, # TODO
action_spec=dataspec.action_spec,
train_step_counter=mock.ANY, # TODO
actor_network=mock_actor_network.return_value,
critic_network=mock_critic_network.return_value,
actor_optimizer=mock.ANY, #TODO
critic_optimizer=mock.ANY, #TODO
td_errors_loss_fn=None,
target_actor_network=None,
target_critic_network=None,
)
self.assertEqual(agent, mock_agent.return_value)
def test_default_config(self, mock_critic_network, mock_actor_network, mock_agent):
params = ["agent.actor_fc_layer_params=[100,10]", "agent.observation_fc_layer_params=[1,2,3]",
"agent.action_fc_layer_params=[1,2,3,4]", "agent.joint_fc_layer_params=[5]",
"agent.ou_stddev=0.1",
"agent.ou_damping=0.2",
"agent.target_update_tau=0.3",
"agent.target_update_period=1",
"agent.dqda_clipping=1.1",
"agent.reward_scale_factor=1.2",
"agent.gradient_clipping=1.3",
"agent.debug_summaries=True",
"agent.summarize_grads_and_vars=True",
"agent.name=Patrick"]
obs_spec = "obs_spec"
dataspec = agent_application.DataSpec(
observation_spec=specs.ArraySpec([1,2,3], int),
action_spec=specs.ArraySpec([1], float)
)
conf = agent_application.make_config(DDPGConfig(), params)
agent_trainer = DDPGAgent(dataspec, conf)
agent = agent_trainer.init_agent()
mock_critic_network.assert_called_once_with((dataspec.observation_spec, dataspec.action_spec),
observation_fc_layer_params=[1,2,3],
action_fc_layer_params=[1,2,3,4],
joint_fc_layer_params=[5])
mock_actor_network.assert_called_once_with(dataspec.observation_spec, dataspec.action_spec,
fc_layer_params=[100,10])
mock_agent.assert_called_once_with(
time_step_spec=mock.ANY, # TODO
action_spec=dataspec.action_spec,
train_step_counter=mock.ANY, # TODO
actor_network=mock_actor_network.return_value,
critic_network=mock_critic_network.return_value,
actor_optimizer=mock.ANY, #TODO
critic_optimizer=mock.ANY, #TODO
td_errors_loss_fn=None,
target_actor_network=None,
target_critic_network=None,
ou_stddev=0.1,
ou_damping=0.2,
target_update_tau=0.3,
target_update_period=1,
dqda_clipping=1.1,
reward_scale_factor=1.2,
gradient_clipping=1.3,
debug_summaries=True,
summarize_grads_and_vars=True,
name="Patrick"
)
self.assertEqual(agent, mock_agent.return_value)
def make_runner(num_runs=4, num_eval_episodes=100, eval_interval=1):
# get run config
params = [
"agent.optimizer.learning_rate=0.01",
"training.num_iterations=10000",
"policy.eps_start=1.0",
"policy.eps_final=0.1",
"agent.fc_layer_params=[100,]",
"trajectory.trajectory_training_window=100",
"project.application_name=cartpole_sim",
"project.dm_storage_path=/tmp/rl_applications/cartpole_sim/%s/" %
int(time.time()),
"project.tensorboard_path=/tmp/tb_path/cartpole_sim/%s" %
datetime.now().strftime("%Y-%m-%dT%H-%M-%S"),
]
conf = agent_application.make_config(QConfig(), params)
print(OmegaConf.to_yaml(conf))
# create batch of cartpole environments
first_timestep_dt = datetime(year=2019, month=8, day=7, hour=10)
training_interval = timedelta(days=1)
spark = get_spark_session()
tfenv = make_batch_tfenv(make_env, conf, first_timestep_dt,
training_interval, spark)
# finalize RL application
data_spec = agent_application.DataSpec(
action_spec=tfenv.action_spec, observation_spec=tfenv.observation_spec)
application = agent_application.AgentApplication(
data_spec=data_spec,
agent=DQNAgent(data_spec, conf),
env=tfenv,
config=conf,
first_timestep_dt=first_timestep_dt,
training_interval=training_interval)
# create the data manager
dm = build_inmemory_data_manager(application)
tfenv.set_dm(dm)
# create simulator runner
sim_runner = SimulationRunner(application=application,
make_eval_env=make_env,
dm=dm,
num_runs=num_runs,
num_eval_episodes=num_eval_episodes,
eval_interval=eval_interval)
return sim_runner
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 make_runner(num_runs=4, num_eval_episodes=100, eval_interval=1):
params = [
"agent.actor_optimizer.learning_rate=1e-4",
"agent.critic_optimizer.learning_rate=1e-3",
"training.num_iterations=2000",
"env.num_envs=60",
"env.num_steps_per_run=50",
"policy.eps_start=1.0",
"policy.eps_final=0.1",
"policy.eps_steps=1000",
"agent.actor_fc_layer_params=[400, 300]",
"agent.observation_fc_layer_params=[400,]",
"agent.joint_fc_layer_params=[300,]",
"trajectory.trajectory_training_window=100",
"project.application_name=mountaincar_ddpg",
"project.dm_storage_path=/tmp/rl_applications/mountaincar_sim/%s/" %
int(time.time()),
"project.tensorboard_path=/tmp/tb_path/mountaincar_sim/%s" %
datetime.now().strftime("%Y-%m-%dT%H-%M-%S"),
]
conf = agent_application.make_config(DDPGConfig(), params)
print(OmegaConf.to_yaml(conf))
first_timestep_dt = datetime(year=2019, month=8, day=7, hour=10)
training_interval = timedelta(days=1)
spark = get_spark_session()
tfenv = make_batch_tfenv(make_env, conf, first_timestep_dt,
training_interval, spark)
data_spec = agent_application.DataSpec(
action_spec=tfenv.action_spec, observation_spec=tfenv.observation_spec)
application = agent_application.AgentApplication(
data_spec=data_spec,
agent=DDPGAgent(data_spec, conf),
env=tfenv,
config=conf,
first_timestep_dt=first_timestep_dt,
training_interval=training_interval)
dm = build_inmemory_data_manager(application)
tfenv.set_dm(dm)
return SimulationRunner(application=application,
make_eval_env=make_env,
dm=dm,
num_runs=num_runs,
num_eval_episodes=num_eval_episodes,
eval_interval=eval_interval)
def setUp(self):
mock_agent = MagicMock(Agent)
dataspec = agent_application.DataSpec(
observation_spec=specs.ArraySpec([1, 2, 3], int),
action_spec=specs.ArraySpec([1], float))
conf = agent_application.make_config(AgentConfig(), [])
today = datetime(date.today().year,
date.today().month,
date.today().day)
env = MagicMock()
self._mock_agent_init = "MOCKED AGENT"
mock_agent.init_agent = MagicMock(return_value=self._mock_agent_init)
self._application = agent_application.AgentApplication(
data_spec=dataspec,
agent=mock_agent,
env=env,
config=conf,
first_timestep_dt=today,
training_interval=timedelta(days=1))
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)
