def test_minibatches_per_step(self):
_epochs = self.epochs
self.epochs = 2
rl_parameters = RLParameters(gamma=0.95,
target_update_rate=0.9,
maxq_learning=True)
rainbow_parameters = RainbowDQNParameters(double_q_learning=True,
dueling_architecture=False)
training_parameters1 = TrainingParameters(
layers=self.layers,
activations=self.activations,
minibatch_size=1024,
minibatches_per_step=1,
learning_rate=0.25,
optimizer="ADAM",
)
training_parameters2 = TrainingParameters(
layers=self.layers,
activations=self.activations,
minibatch_size=128,
minibatches_per_step=8,
learning_rate=0.25,
optimizer="ADAM",
)
env1 = Env(self.state_dims, self.action_dims)
env2 = Env(self.state_dims, self.action_dims)
model_parameters1 = DiscreteActionModelParameters(
actions=env1.actions,
rl=rl_parameters,
rainbow=rainbow_parameters,
training=training_parameters1,
)
model_parameters2 = DiscreteActionModelParameters(
actions=env2.actions,
rl=rl_parameters,
rainbow=rainbow_parameters,
training=training_parameters2,
)
# minibatch_size / 8, minibatches_per_step * 8 should give the same result
logger.info("Training model 1")
trainer1 = self._train(model_parameters1, env1)
SummaryWriterContext._reset_globals()
logger.info("Training model 2")
trainer2 = self._train(model_parameters2, env2)
weight1 = trainer1.q_network.fc.layers[-1].weight.detach().numpy()
weight2 = trainer2.q_network.fc.layers[-1].weight.detach().numpy()
# Due to numerical stability this tolerance has to be fairly high
self.assertTrue(np.allclose(weight1, weight2, rtol=0.0, atol=1e-3))
self.epochs = _epochs
def test_trainer_maxq(self):
env = Env(self.state_dims, self.action_dims)
env.seed(42)
maxq_parameters = DiscreteActionModelParameters(
actions=env.actions,
rl=RLParameters(
gamma=0.99,
target_update_rate=1.0,
reward_burnin=100,
maxq_learning=True,
),
training=TrainingParameters(
layers=self.layers,
activations=self.activations,
minibatch_size=self.minibatch_size,
learning_rate=1.0,
optimizer="ADAM",
),
)
maxq_trainer = DiscreteActionTrainer(maxq_parameters,
env.normalization)
# predictor = maxq_trainer.predictor()
logger.info("Generating constant_reward MDPs..")
states, actions, rewards, next_states, next_actions, is_terminal, possible_next_actions = env.generate_samples_discrete(
self.num_samples)
logger.info("Preprocessing constant_reward MDPs..")
tdps = env.preprocess_samples_discrete(
states,
actions,
rewards,
next_states,
next_actions,
is_terminal,
possible_next_actions,
self.minibatch_size,
)
for epoch in range(self.epochs):
logger.info("Training.. " + str(epoch))
for tdp in tdps:
maxq_trainer.train_numpy(tdp, None)
logger.info(" ".join([
"Training epoch",
str(epoch),
"average q values",
str(np.mean(workspace.FetchBlob(maxq_trainer.q_score_output))),
"td_loss",
str(workspace.FetchBlob(maxq_trainer.loss_blob)),
]))
# Q value should converge to very close to 100
avg_q_value_after_training = np.mean(
workspace.FetchBlob(maxq_trainer.q_score_output))
self.assertLess(avg_q_value_after_training, 101)
self.assertGreater(avg_q_value_after_training, 99)
def get_sarsa_trainer_reward_boost(self, environment, reward_shape):
rl_parameters = RLParameters(
gamma=DISCOUNT,
target_update_rate=1.0,
reward_burnin=10,
maxq_learning=False,
reward_boost=reward_shape,
)
training_parameters = TrainingParameters(
layers=[-1, -1],
activations=["linear"],
minibatch_size=self.minibatch_size,
learning_rate=0.125,
optimizer="ADAM",
)
return DiscreteActionTrainer(
DiscreteActionModelParameters(
actions=environment.ACTIONS,
rl=rl_parameters,
training=training_parameters,
rainbow=RainbowDQNParameters(double_q_learning=True,
dueling_architecture=False),
in_training_cpe=InTrainingCPEParameters(mdp_sampled_rate=0.1),
),
environment.normalization,
)
def test_no_soft_update(self):
model = Model()
target_model = copy.deepcopy(model)
for target_param, param in zip(model.parameters(),
target_model.parameters()):
self.assertIs(target_param, param)
optimizer = torch.optim.Adam(model.parameters())
x = torch.tensor([1, 2], dtype=torch.int64)
emb = model(x)
loss = emb.sum()
loss.backward()
optimizer.step()
params = list(model.parameters())
self.assertEqual(1, len(params))
param = params[0].detach().numpy()
trainer = RLTrainer(DiscreteActionModelParameters(rl=RLParameters()),
use_gpu=False)
trainer._soft_update(model, target_model, 0.1)
target_params = list(target_model.parameters())
self.assertEqual(1, len(target_params))
target_param = target_params[0].detach().numpy()
npt.assert_array_equal(target_param, param)
def get_sarsa_trainer_reward_boost(
self,
environment,
reward_shape,
dueling,
use_gpu=False,
use_all_avail_gpus=False,
):
rl_parameters = RLParameters(
gamma=DISCOUNT,
target_update_rate=1.0,
reward_burnin=10,
maxq_learning=False,
reward_boost=reward_shape,
)
training_parameters = TrainingParameters(
layers=[-1, 128, -1] if dueling else [-1, -1],
activations=["relu", "linear"] if dueling else ["linear"],
minibatch_size=self.minibatch_size,
learning_rate=0.05,
optimizer="ADAM",
)
return DQNTrainer(
DiscreteActionModelParameters(
actions=environment.ACTIONS,
rl=rl_parameters,
training=training_parameters,
rainbow=RainbowDQNParameters(
double_q_learning=True, dueling_architecture=dueling
),
),
environment.normalization,
use_gpu=use_gpu,
use_all_avail_gpus=use_all_avail_gpus,
)
def get_sarsa_parameters(self, environment, reward_shape, dueling,
categorical, clip_grad_norm):
rl_parameters = RLParameters(
gamma=DISCOUNT,
target_update_rate=1.0,
maxq_learning=False,
reward_boost=reward_shape,
)
training_parameters = TrainingParameters(
layers=[-1, 128, -1] if dueling else [-1, -1],
activations=["relu", "linear"] if dueling else ["linear"],
minibatch_size=self.minibatch_size,
learning_rate=0.05,
optimizer="ADAM",
clip_grad_norm=clip_grad_norm,
)
return DiscreteActionModelParameters(
actions=environment.ACTIONS,
rl=rl_parameters,
training=training_parameters,
rainbow=RainbowDQNParameters(
double_q_learning=True,
dueling_architecture=dueling,
categorical=categorical,
),
)
def test_trainer_maxq(self):
env = Env(self.state_dims, self.action_dims)
env.seed(42)
maxq_parameters = DiscreteActionModelParameters(
actions=env.actions,
rl=RLParameters(
gamma=0.99,
target_update_rate=0.9,
reward_burnin=100,
maxq_learning=True,
),
rainbow=RainbowDQNParameters(double_q_learning=True,
dueling_architecture=False),
training=TrainingParameters(
layers=self.layers,
activations=self.activations,
minibatch_size=self.minibatch_size,
learning_rate=0.25,
optimizer="ADAM",
),
)
maxq_trainer = DQNTrainer(maxq_parameters, env.normalization)
logger.info("Generating constant_reward MDPs..")
states, actions, rewards, next_states, next_actions, is_terminal, possible_actions, possible_next_actions = env.generate_samples_discrete(
self.num_samples)
logger.info("Preprocessing constant_reward MDPs..")
for epoch in range(self.epochs):
tdps = env.preprocess_samples_discrete(
states,
actions,
rewards,
next_states,
next_actions,
is_terminal,
possible_actions,
possible_next_actions,
self.minibatch_size,
)
logger.info("Training.. " + str(epoch))
for tdp in tdps:
maxq_trainer.train(tdp)
logger.info(" ".join([
"Training epoch",
str(epoch),
"average q values",
str(torch.mean(maxq_trainer.all_action_scores)),
]))
# Q value should converge to very close to 100
avg_q_value_after_training = torch.mean(maxq_trainer.all_action_scores)
self.assertLess(avg_q_value_after_training, 101)
self.assertGreater(avg_q_value_after_training, 99)
def test_pure_q_learning_all_cheat(self):
q_learning_parameters = DiscreteActionModelParameters(
actions=self._env.ACTIONS,
rl=self._rl_parameters_all_cheat_maxq,
training=TrainingParameters(
layers=[self._env.width * self._env.height, 1],
activations=['linear'],
minibatch_size=self.minibatch_size,
learning_rate=0.05,
optimizer='SGD',
lr_policy='fixed',
)
)
trainer = DiscreteActionTrainer(
q_learning_parameters,
self._env.normalization,
)
predictor = trainer.predictor()
policy = _build_policy(self._env, predictor, 1)
initial_state = self._env.reset()
iteration_result = _collect_samples(
self._env, policy, 20000, initial_state
)
num_iterations = 50
for _ in range(num_iterations):
tdps = self._env.preprocess_samples(
iteration_result.states,
iteration_result.actions,
iteration_result.rewards,
iteration_result.next_states,
iteration_result.next_actions,
iteration_result.is_terminals,
iteration_result.possible_next_actions,
None,
self.minibatch_size,
)
for tdp in tdps:
trainer.train_numpy(tdp, None)
initial_state = self._env.reset()
policy = _build_policy(self._env, predictor, 0.1)
iteration_result = _collect_samples(
self._env, policy, 20000, initial_state
)
policy = _build_policy(self._env, predictor, 0)
initial_state = self._env.reset()
iteration_result = _collect_samples(
self._env, policy, 1000, initial_state
)
# 100% should be cheat. Will fix in the future.
self.assertGreater(
np.sum(np.array(iteration_result.actions) == 'C'), 800
)
def main(params):
# Set minibatch size based on # of devices being used to train
params["training"]["minibatch_size"] *= minibatch_size_multiplier(
params["use_gpu"], params["use_all_avail_gpus"])
rl_parameters = RLParameters(**params["rl"])
training_parameters = TrainingParameters(**params["training"])
rainbow_parameters = RainbowDQNParameters(**params["rainbow"])
model_params = DiscreteActionModelParameters(
actions=params["actions"],
rl=rl_parameters,
training=training_parameters,
rainbow=rainbow_parameters,
)
state_normalization = BaseWorkflow.read_norm_file(
params["state_norm_data_path"])
writer = SummaryWriter(log_dir=params["model_output_path"])
logger.info("TensorBoard logging location is: {}".format(writer.log_dir))
preprocess_handler = DqnPreprocessHandler(
Preprocessor(state_normalization, False),
np.array(model_params.actions),
PandasSparseToDenseProcessor(),
)
workflow = DqnWorkflow(
model_params,
preprocess_handler,
state_normalization,
params["use_gpu"],
params["use_all_avail_gpus"],
)
train_dataset = JSONDatasetReader(
params["training_data_path"],
batch_size=training_parameters.minibatch_size)
eval_dataset = JSONDatasetReader(params["eval_data_path"], batch_size=16)
with summary_writer_context(writer):
workflow.train_network(train_dataset, eval_dataset,
int(params["epochs"]))
exporter = DQNExporter(
workflow.trainer.q_network,
PredictorFeatureExtractor(
state_normalization_parameters=state_normalization),
DiscreteActionOutputTransformer(model_params.actions),
)
return export_trainer_and_predictor(workflow.trainer,
params["model_output_path"],
exporter=exporter) # noqa
def test_trainer_maxq(self):
environment = Gridworld()
maxq_sarsa_parameters = DiscreteActionModelParameters(
actions=environment.ACTIONS,
rl=RLParameters(
gamma=DISCOUNT,
target_update_rate=0.5,
reward_burnin=10,
maxq_learning=True,
),
training=TrainingParameters(
layers=[-1, 1],
activations=["linear"],
minibatch_size=self.minibatch_size,
learning_rate=0.01,
optimizer="ADAM",
),
)
# construct the new trainer that using maxq
maxq_trainer = DiscreteActionTrainer(
maxq_sarsa_parameters, environment.normalization
)
samples = environment.generate_samples(100000, 1.0)
predictor = maxq_trainer.predictor()
tdps = environment.preprocess_samples(samples, self.minibatch_size)
evaluator = GridworldEvaluator(environment, True)
evaluator.evaluate(predictor)
print(
"Pre-Training eval: ",
evaluator.mc_loss[-1],
evaluator.reward_doubly_robust[-1],
)
self.assertGreater(evaluator.mc_loss[-1], 0.3)
for _ in range(5):
for tdp in tdps:
maxq_trainer.train_numpy(tdp, None)
evaluator.evaluate(predictor)
print(
"Post-Training eval: ",
evaluator.mc_loss[-1],
evaluator.reward_doubly_robust[-1],
)
self.assertLess(evaluator.mc_loss[-1], 0.1)
self.assertGreater(
evaluator.reward_doubly_robust[-1], evaluator.reward_doubly_robust[-2]
)
def test_trainer_maxq(self):
environment = Gridworld()
maxq_sarsa_parameters = DiscreteActionModelParameters(
actions=environment.ACTIONS,
rl=RLParameters(gamma=DISCOUNT,
target_update_rate=0.5,
reward_burnin=10,
maxq_learning=True),
training=TrainingParameters(
layers=[-1, 1],
activations=['linear'],
minibatch_size=self.minibatch_size,
learning_rate=0.01,
optimizer='ADAM',
))
# construct the new trainer that using maxq
maxq_trainer = DiscreteActionTrainer(
maxq_sarsa_parameters,
environment.normalization,
)
states, actions, rewards, next_states, next_actions, is_terminal,\
possible_next_actions, reward_timelines = \
environment.generate_samples(100000, 1.0)
predictor = maxq_trainer.predictor()
tdps = environment.preprocess_samples(
states,
actions,
rewards,
next_states,
next_actions,
is_terminal,
possible_next_actions,
reward_timelines,
self.minibatch_size,
)
evaluator = GridworldEvaluator(environment, True)
print("Pre-Training eval", evaluator.evaluate(predictor))
self.assertGreater(evaluator.evaluate(predictor), 0.3)
for _ in range(2):
for tdp in tdps:
maxq_trainer.stream_tdp(tdp, None)
evaluator.evaluate(predictor)
print("Post-Training eval", evaluator.evaluate(predictor))
self.assertLess(evaluator.evaluate(predictor), 0.1)
def train_network(params):
logger.info("Running DQN workflow with params:")
logger.info(params)
action_names = np.array(params["actions"])
rl_parameters = RLParameters(**params["rl"])
training_parameters = TrainingParameters(**params["training"])
rainbow_parameters = RainbowDQNParameters(**params["rainbow"])
trainer_params = DiscreteActionModelParameters(
actions=params["actions"],
rl=rl_parameters,
training=training_parameters,
rainbow=rainbow_parameters,
)
dataset = JSONDataset(params["training_data_path"],
batch_size=training_parameters.minibatch_size)
norm_data = JSONDataset(params["state_norm_data_path"])
state_normalization = read_norm_params(norm_data.read_all())
num_batches = int(len(dataset) / training_parameters.minibatch_size)
logger.info("Read in batch data set {} of size {} examples. Data split "
"into {} batches of size {}.".format(
params["training_data_path"],
len(dataset),
num_batches,
training_parameters.minibatch_size,
))
trainer = DQNTrainer(trainer_params, state_normalization,
params["use_gpu"])
for epoch in range(params["epochs"]):
for batch_idx in range(num_batches):
helpers.report_training_status(batch_idx, num_batches, epoch,
params["epochs"])
batch = dataset.read_batch(batch_idx)
tdp = preprocess_batch_for_training(action_names, batch,
state_normalization)
trainer.train(tdp)
logger.info("Training finished. Saving PyTorch model to {}".format(
params["pytorch_output_path"]))
helpers.save_model_to_file(trainer, params["pytorch_output_path"])
def get_sarsa_trainer(self, environment):
rl_parameters = RLParameters(gamma=DISCOUNT,
target_update_rate=0.5,
reward_burnin=10,
maxq_learning=False)
training_parameters = TrainingParameters(
layers=[-1, 1],
activations=['linear'],
minibatch_size=1024,
learning_rate=0.01,
optimizer='ADAM',
)
return DiscreteActionTrainer(
environment.normalization,
DiscreteActionModelParameters(actions=environment.ACTIONS,
rl=rl_parameters,
training=training_parameters))
def test_sarsa_layer_validation(self):
env = Gridworld()
invalid_sarsa_params = DiscreteActionModelParameters(
actions=env.ACTIONS,
rl=RLParameters(gamma=DISCOUNT,
target_update_rate=0.5,
reward_burnin=10,
maxq_learning=False),
training=TrainingParameters(
layers=[-1, 3],
activations=['linear'],
minibatch_size=32,
learning_rate=0.1,
optimizer='SGD',
))
with self.assertRaises(Exception):
# layers[-1] should be 1
DiscreteActionTrainer(env.normalization, invalid_sarsa_params)
def main(args):
parser = argparse.ArgumentParser(
description="Train a RL net to play in an OpenAI Gym environment.")
parser.add_argument("-p",
"--parameters",
help="Path to JSON parameters file.")
parser.add_argument("-s",
"--score-bar",
help="Bar for averaged tests scores.",
type=float,
default=None)
parser.add_argument(
"-g",
"--gpu_id",
help="If set, will use GPU with specified ID. Otherwise will use CPU.",
default=USE_CPU)
args = parser.parse_args(args)
with open(args.parameters, 'r') as f:
params = json.load(f)
rl_settings = params['rl']
training_settings = params['training']
rl_settings['gamma'] = rl_settings['reward_discount_factor']
del rl_settings['reward_discount_factor']
training_settings['gamma'] = training_settings['learning_rate_decay']
del training_settings['learning_rate_decay']
env_type = params['env']
env = OpenAIGymEnvironment(env_type, rl_settings['epsilon'])
trainer_params = DiscreteActionModelParameters(
actions=env.actions,
rl=RLParameters(**rl_settings),
training=TrainingParameters(**training_settings))
device = core.DeviceOption(
caffe2_pb2.CPU if args.gpu_id == USE_CPU else caffe2_pb2.CUDA,
args.gpu_id)
with core.DeviceScope(device):
trainer = DiscreteActionTrainer(env.normalization,
trainer_params,
skip_normalization=True)
return run(env, trainer, "{} test run".format(env_type),
args.score_bar, **params["run_details"])
def test_pure_q_learning_all_cheat(self):
q_learning_parameters = DiscreteActionModelParameters(
actions=self._env.ACTIONS,
rl=self._rl_parameters_all_cheat_maxq,
training=TrainingParameters(
layers=[self._env.width * self._env.height, 1],
activations=['linear'],
minibatch_size=32,
learning_rate=0.05,
optimizer='SGD',
lr_policy='fixed'))
trainer = DiscreteActionTrainer(self._env.normalization,
q_learning_parameters)
predictor = trainer.predictor()
policy = _build_policy(self._env, predictor, 1)
initial_state = self._env.reset()
iteration_result = _collect_samples(self._env, policy, 10000,
initial_state)
num_iterations = 50
for _ in range(num_iterations):
policy = _build_policy(self._env, predictor, 0)
tdp = self._env.preprocess_samples(
iteration_result.states,
iteration_result.actions,
iteration_result.rewards,
iteration_result.next_states,
iteration_result.next_actions,
iteration_result.is_terminals,
iteration_result.possible_next_actions,
None,
)
trainer.stream_tdp(tdp, None)
initial_state = iteration_result.current_state
initial_state = self._env.reset()
iteration_result = _collect_samples(self._env, policy, 10000,
initial_state)
self.assertTrue(np.all(np.array(iteration_result.actions) == 'C'))
def get_sarsa_trainer_reward_boost(self, environment, reward_shape):
rl_parameters = RLParameters(
gamma=DISCOUNT,
target_update_rate=0.5,
reward_burnin=10,
maxq_learning=False,
reward_boost=reward_shape,
)
training_parameters = TrainingParameters(
layers=[-1, -1],
activations=["linear"],
minibatch_size=self.minibatch_size,
learning_rate=0.01,
optimizer="ADAM",
)
return DiscreteActionTrainer(
DiscreteActionModelParameters(
actions=environment.ACTIONS,
rl=rl_parameters,
training=training_parameters,
),
environment.normalization,
)
def test_trainer_maxq(self):
env = Env(self.state_dims, self.action_dims)
maxq_parameters = DiscreteActionModelParameters(
actions=env.actions,
rl=RLParameters(gamma=0.95,
target_update_rate=0.9,
maxq_learning=True),
rainbow=RainbowDQNParameters(double_q_learning=True,
dueling_architecture=False),
training=TrainingParameters(
layers=self.layers,
activations=self.activations,
minibatch_size=1024,
learning_rate=0.25,
optimizer="ADAM",
),
)
# Q value should converge to very close to 20
trainer = self._train(maxq_parameters, env)
avg_q_value_after_training = torch.mean(trainer.all_action_scores)
self.assertLess(avg_q_value_after_training, 22)
self.assertGreater(avg_q_value_after_training, 18)
def run_gym(params, score_bar, gpu_id):
rl_settings = params['rl']
training_settings = params['training']
rl_settings['gamma'] = rl_settings['reward_discount_factor']
del rl_settings['reward_discount_factor']
training_settings['gamma'] = training_settings['learning_rate_decay']
del training_settings['learning_rate_decay']
env_type = params['env']
env = OpenAIGymEnvironment(env_type, rl_settings['epsilon'])
trainer_params = DiscreteActionModelParameters(
actions=env.actions,
rl=RLParameters(**rl_settings),
training=TrainingParameters(**training_settings))
device = core.DeviceOption(
caffe2_pb2.CPU if gpu_id == USE_CPU else caffe2_pb2.CUDA,
gpu_id,
)
with core.DeviceScope(device):
if env.img:
trainer = DiscreteActionConvTrainer(
DiscreteActionConvModelParameters(
fc_parameters=trainer_params,
cnn_parameters=CNNModelParameters(**params['cnn']),
num_input_channels=env.num_input_channels,
img_height=env.height,
img_width=env.width),
env.normalization,
)
else:
trainer = DiscreteActionTrainer(
trainer_params,
env.normalization,
)
return run(env, trainer, "{} test run".format(env_type), score_bar,
**params["run_details"])
def run_gym(params, score_bar, gpu_id):
rl_settings = params['rl']
rl_settings['gamma'] = rl_settings['reward_discount_factor']
del rl_settings['reward_discount_factor']
env_type = params['env']
env = OpenAIGymEnvironment(env_type, rl_settings['epsilon'])
model_type = params['model_type']
c2_device = core.DeviceOption(
caffe2_pb2.CPU if gpu_id == USE_CPU else caffe2_pb2.CUDA,
gpu_id,
)
if model_type == ModelType.DISCRETE_ACTION.value:
with core.DeviceScope(c2_device):
training_settings = params['training']
training_settings['gamma'] = training_settings[
'learning_rate_decay']
del training_settings['learning_rate_decay']
trainer_params = DiscreteActionModelParameters(
actions=env.actions,
rl=RLParameters(**rl_settings),
training=TrainingParameters(**training_settings))
if env.img:
trainer = DiscreteActionConvTrainer(
DiscreteActionConvModelParameters(
fc_parameters=trainer_params,
cnn_parameters=CNNModelParameters(**params['cnn']),
num_input_channels=env.num_input_channels,
img_height=env.height,
img_width=env.width),
env.normalization,
)
else:
trainer = DiscreteActionTrainer(
trainer_params,
env.normalization,
)
elif model_type == ModelType.PARAMETRIC_ACTION.value:
with core.DeviceScope(c2_device):
training_settings = params['training']
training_settings['gamma'] = training_settings[
'learning_rate_decay']
del training_settings['learning_rate_decay']
trainer_params = ContinuousActionModelParameters(
rl=RLParameters(**rl_settings),
training=TrainingParameters(**training_settings),
knn=KnnParameters(model_type='DQN', ),
)
trainer = ContinuousActionDQNTrainer(trainer_params,
env.normalization,
env.normalization_action)
elif model_type == ModelType.CONTINUOUS_ACTION.value:
training_settings = params['shared_training']
training_settings['gamma'] = training_settings['learning_rate_decay']
del training_settings['learning_rate_decay']
actor_settings = params['actor_training']
critic_settings = params['critic_training']
trainer_params = DDPGModelParameters(
rl=DDPGRLParameters(**rl_settings),
shared_training=DDPGTrainingParameters(**training_settings),
actor_training=DDPGNetworkParameters(**actor_settings),
critic_training=DDPGNetworkParameters(**critic_settings),
)
trainer = DDPGTrainer(
trainer_params,
EnvDetails(
state_dim=env.state_dim,
action_dim=env.action_dim,
action_range=(env.action_space.low, env.action_space.high),
))
else:
raise NotImplementedError(
"Model of type {} not supported".format(model_type))
return run(env, model_type, trainer, "{} test run".format(env_type),
score_bar, **params["run_details"])
def create_trainer(model_type, params, rl_parameters, use_gpu, env):
if model_type == ModelType.PYTORCH_DISCRETE_DQN.value:
training_parameters = params["training"]
if isinstance(training_parameters, dict):
training_parameters = TrainingParameters(**training_parameters)
rainbow_parameters = params["rainbow"]
if isinstance(rainbow_parameters, dict):
rainbow_parameters = RainbowDQNParameters(**rainbow_parameters)
if env.img:
assert (training_parameters.cnn_parameters
is not None), "Missing CNN parameters for image input"
if isinstance(training_parameters.cnn_parameters, dict):
training_parameters.cnn_parameters = CNNParameters(
**training_parameters.cnn_parameters)
training_parameters.cnn_parameters.conv_dims[
0] = env.num_input_channels
training_parameters.cnn_parameters.input_height = env.height
training_parameters.cnn_parameters.input_width = env.width
training_parameters.cnn_parameters.num_input_channels = (
env.num_input_channels)
else:
assert (training_parameters.cnn_parameters is
None), "Extra CNN parameters for non-image input"
trainer_params = DiscreteActionModelParameters(
actions=env.actions,
rl=rl_parameters,
training=training_parameters,
rainbow=rainbow_parameters,
)
trainer = DQNTrainer(trainer_params, env.normalization, use_gpu)
elif model_type == ModelType.PYTORCH_PARAMETRIC_DQN.value:
training_parameters = params["training"]
if isinstance(training_parameters, dict):
training_parameters = TrainingParameters(**training_parameters)
rainbow_parameters = params["rainbow"]
if isinstance(rainbow_parameters, dict):
rainbow_parameters = RainbowDQNParameters(**rainbow_parameters)
if env.img:
assert (training_parameters.cnn_parameters
is not None), "Missing CNN parameters for image input"
training_parameters.cnn_parameters.conv_dims[
0] = env.num_input_channels
else:
assert (training_parameters.cnn_parameters is
None), "Extra CNN parameters for non-image input"
trainer_params = ContinuousActionModelParameters(
rl=rl_parameters,
training=training_parameters,
rainbow=rainbow_parameters)
trainer = ParametricDQNTrainer(trainer_params, env.normalization,
env.normalization_action, use_gpu)
elif model_type == ModelType.CONTINUOUS_ACTION.value:
training_parameters = params["shared_training"]
if isinstance(training_parameters, dict):
training_parameters = DDPGTrainingParameters(**training_parameters)
actor_parameters = params["actor_training"]
if isinstance(actor_parameters, dict):
actor_parameters = DDPGNetworkParameters(**actor_parameters)
critic_parameters = params["critic_training"]
if isinstance(critic_parameters, dict):
critic_parameters = DDPGNetworkParameters(**critic_parameters)
trainer_params = DDPGModelParameters(
rl=rl_parameters,
shared_training=training_parameters,
actor_training=actor_parameters,
critic_training=critic_parameters,
)
action_range_low = env.action_space.low.astype(np.float32)
action_range_high = env.action_space.high.astype(np.float32)
trainer = DDPGTrainer(
trainer_params,
env.normalization,
env.normalization_action,
torch.from_numpy(action_range_low).unsqueeze(dim=0),
torch.from_numpy(action_range_high).unsqueeze(dim=0),
use_gpu,
)
elif model_type == ModelType.SOFT_ACTOR_CRITIC.value:
trainer_params = SACModelParameters(
rl=rl_parameters,
training=SACTrainingParameters(
minibatch_size=params["sac_training"]["minibatch_size"],
use_2_q_functions=params["sac_training"]["use_2_q_functions"],
q_network_optimizer=OptimizerParameters(
**params["sac_training"]["q_network_optimizer"]),
value_network_optimizer=OptimizerParameters(
**params["sac_training"]["value_network_optimizer"]),
actor_network_optimizer=OptimizerParameters(
**params["sac_training"]["actor_network_optimizer"]),
entropy_temperature=params["sac_training"]
["entropy_temperature"],
),
q_network=FeedForwardParameters(**params["sac_q_training"]),
value_network=FeedForwardParameters(
**params["sac_value_training"]),
actor_network=FeedForwardParameters(
**params["sac_actor_training"]),
)
trainer = get_sac_trainer(env, trainer_params, use_gpu)
else:
raise NotImplementedError(
"Model of type {} not supported".format(model_type))
return trainer
def test_q_learning_limited(self):
# TODO: This model oscilliates pretty bad, will investigate in the future.
target_cheat_percentage = 50
epsilon = 0.2
num_iterations = 30
self.minibatch_size = 1024
num_steps = self.minibatch_size * 10
updates_per_iteration = 1
q_learning_parameters = DiscreteActionModelParameters(
actions=self._env.ACTIONS,
rl=self._rl_parameters_maxq,
training=TrainingParameters(
layers=[-1, -1],
activations=["linear"],
minibatch_size=self.minibatch_size,
learning_rate=0.05,
optimizer="ADAM",
),
action_budget=ActionBudget(
limited_action="C",
action_limit=target_cheat_percentage,
quantile_update_rate=0.2,
quantile_update_frequency=1,
window_size=1000,
),
)
trainer = LimitedActionDiscreteActionTrainer(q_learning_parameters,
self._env.normalization)
predictor = trainer.predictor()
policy = _build_policy(self._env, predictor, epsilon)
initial_state = self._env.reset()
for iteration in range(num_iterations):
policy = _build_policy(self._env, predictor, epsilon)
iteration_result = _collect_samples(self._env, policy, num_steps,
initial_state)
tdps = self._env.preprocess_samples(
iteration_result.states,
iteration_result.actions,
iteration_result.propensities,
iteration_result.rewards,
iteration_result.next_states,
iteration_result.next_actions,
iteration_result.is_terminals,
iteration_result.possible_next_actions,
None,
self.minibatch_size,
)
print("iter: {} ({}), ratio: {}, steps to solve: {}, quantile: {}".
format(
iteration,
num_steps,
iteration_result.cheat_ratio,
np.mean(iteration_result.lengths),
trainer.quantile_value,
))
initial_state = iteration_result.current_state
for _ in range(updates_per_iteration):
for tdp in tdps:
trainer.train_numpy(tdp, None)
state = self._env.reset()
evaluation_results = _collect_samples(self._env, policy, 10000, state)
print(
np.sum(np.array(evaluation_results.lengths) <= 14) /
len(evaluation_results.lengths))
optimality_ratio = np.sum(
np.array(evaluation_results.lengths) <= 14) / len(
evaluation_results.lengths)
self.assertGreater(optimality_ratio, 0.5)
accuracy = np.abs(evaluation_results.cheat_ratio -
target_cheat_percentage / 100)
print("ACCURACY", evaluation_results.cheat_ratio,
target_cheat_percentage)
self.assertTrue(
accuracy <
0.4) # TODO: Would like to get this accuracy up in the future
def run_gym(params, score_bar, gpu_id, save_timesteps_to_dataset=None):
logger.info("Running gym with params")
logger.info(params)
rl_parameters = RLParameters(**params["rl"])
env_type = params["env"]
env = OpenAIGymEnvironment(
env_type,
rl_parameters.epsilon,
rl_parameters.softmax_policy,
params["max_replay_memory_size"],
)
model_type = params["model_type"]
c2_device = core.DeviceOption(
caffe2_pb2.CPU if gpu_id == USE_CPU else caffe2_pb2.CUDA, gpu_id
)
if model_type == ModelType.DISCRETE_ACTION.value:
with core.DeviceScope(c2_device):
training_settings = params["training"]
training_parameters = TrainingParameters(**training_settings)
if env.img:
assert (
training_parameters.cnn_parameters is not None
), "Missing CNN parameters for image input"
training_parameters.cnn_parameters = CNNParameters(
**training_settings["cnn_parameters"]
)
training_parameters.cnn_parameters.conv_dims[0] = env.num_input_channels
training_parameters.cnn_parameters.input_height = env.height
training_parameters.cnn_parameters.input_width = env.width
training_parameters.cnn_parameters.num_input_channels = (
env.num_input_channels
)
else:
assert (
training_parameters.cnn_parameters is None
), "Extra CNN parameters for non-image input"
trainer_params = DiscreteActionModelParameters(
actions=env.actions, rl=rl_parameters, training=training_parameters
)
trainer = DiscreteActionTrainer(trainer_params, env.normalization)
elif model_type == ModelType.PARAMETRIC_ACTION.value:
with core.DeviceScope(c2_device):
training_settings = params["training"]
training_parameters = TrainingParameters(**training_settings)
if env.img:
assert (
training_parameters.cnn_parameters is not None
), "Missing CNN parameters for image input"
training_parameters.cnn_parameters = CNNParameters(
**training_settings["cnn_parameters"]
)
training_parameters.cnn_parameters.conv_dims[0] = env.num_input_channels
else:
assert (
training_parameters.cnn_parameters is None
), "Extra CNN parameters for non-image input"
trainer_params = ContinuousActionModelParameters(
rl=rl_parameters,
training=training_parameters,
knn=KnnParameters(model_type="DQN"),
)
trainer = ContinuousActionDQNTrainer(
trainer_params, env.normalization, env.normalization_action
)
elif model_type == ModelType.CONTINUOUS_ACTION.value:
training_settings = params["shared_training"]
actor_settings = params["actor_training"]
critic_settings = params["critic_training"]
trainer_params = DDPGModelParameters(
rl=rl_parameters,
shared_training=DDPGTrainingParameters(**training_settings),
actor_training=DDPGNetworkParameters(**actor_settings),
critic_training=DDPGNetworkParameters(**critic_settings),
)
# DDPG can handle continuous and discrete action spaces
if env.action_type == EnvType.CONTINUOUS_ACTION:
action_range = env.action_space.high
else:
action_range = None
trainer = DDPGTrainer(
trainer_params,
env.normalization,
env.normalization_action,
use_gpu=False,
action_range=action_range,
)
else:
raise NotImplementedError("Model of type {} not supported".format(model_type))
return run(
c2_device,
env,
model_type,
trainer,
"{} test run".format(env_type),
score_bar,
**params["run_details"],
save_timesteps_to_dataset=save_timesteps_to_dataset,
)
def train_network(params):
writer = None
if params["model_output_path"] is not None:
writer = SummaryWriter(log_dir=params["model_output_path"])
logger.info("Running DQN workflow with params:")
logger.info(params)
# Set minibatch size based on # of devices being used to train
params["training"]["minibatch_size"] *= minibatch_size_multiplier(
params["use_gpu"], params["use_all_avail_gpus"])
action_names = np.array(params["actions"])
rl_parameters = RLParameters(**params["rl"])
training_parameters = TrainingParameters(**params["training"])
rainbow_parameters = RainbowDQNParameters(**params["rainbow"])
trainer_params = DiscreteActionModelParameters(
actions=params["actions"],
rl=rl_parameters,
training=training_parameters,
rainbow=rainbow_parameters,
)
dataset = JSONDataset(params["training_data_path"],
batch_size=training_parameters.minibatch_size)
eval_dataset = JSONDataset(params["eval_data_path"], batch_size=16)
state_normalization = read_norm_file(params["state_norm_data_path"])
num_batches = int(len(dataset) / training_parameters.minibatch_size)
logger.info("Read in batch data set {} of size {} examples. Data split "
"into {} batches of size {}.".format(
params["training_data_path"],
len(dataset),
num_batches,
training_parameters.minibatch_size,
))
trainer = DQNTrainer(
trainer_params,
state_normalization,
use_gpu=params["use_gpu"],
use_all_avail_gpus=params["use_all_avail_gpus"],
)
trainer = update_model_for_warm_start(trainer)
preprocessor = Preprocessor(state_normalization, False)
evaluator = Evaluator(
trainer_params.actions,
trainer_params.rl.gamma,
trainer,
metrics_to_score=trainer.metrics_to_score,
)
start_time = time.time()
for epoch in range(int(params["epochs"])):
dataset.reset_iterator()
for batch_idx in range(num_batches):
report_training_status(batch_idx, num_batches, epoch,
int(params["epochs"]))
batch = dataset.read_batch(batch_idx)
tdp = preprocess_batch_for_training(preprocessor, batch,
action_names)
tdp.set_type(trainer.dtype)
trainer.train(tdp)
eval_dataset.reset_iterator()
accumulated_edp = None
while True:
batch = eval_dataset.read_batch(batch_idx)
if batch is None:
break
tdp = preprocess_batch_for_training(preprocessor, batch,
action_names)
edp = EvaluationDataPage.create_from_tdp(tdp, trainer)
if accumulated_edp is None:
accumulated_edp = edp
else:
accumulated_edp = accumulated_edp.append(edp)
accumulated_edp = accumulated_edp.compute_values(trainer.gamma)
cpe_start_time = time.time()
details = evaluator.evaluate_post_training(accumulated_edp)
details.log()
logger.info("CPE evaluation took {} seconds.".format(time.time() -
cpe_start_time))
through_put = (len(dataset) * int(params["epochs"])) / (time.time() -
start_time)
logger.info("Training finished. Processed ~{} examples / s.".format(
round(through_put)))
if writer is not None:
writer.close()
return export_trainer_and_predictor(trainer, params["model_output_path"])
def create_trainer(model_type, params, rl_parameters, use_gpu, env):
if model_type == ModelType.PYTORCH_DISCRETE_DQN.value:
training_parameters = params["training"]
if isinstance(training_parameters, dict):
training_parameters = TrainingParameters(**training_parameters)
rainbow_parameters = params["rainbow"]
if isinstance(rainbow_parameters, dict):
rainbow_parameters = RainbowDQNParameters(**rainbow_parameters)
if env.img:
assert (
training_parameters.cnn_parameters is not None
), "Missing CNN parameters for image input"
if isinstance(training_parameters.cnn_parameters, dict):
training_parameters.cnn_parameters = CNNParameters(
**training_parameters.cnn_parameters
)
training_parameters.cnn_parameters.conv_dims[0] = env.num_input_channels
training_parameters.cnn_parameters.input_height = env.height
training_parameters.cnn_parameters.input_width = env.width
training_parameters.cnn_parameters.num_input_channels = (
env.num_input_channels
)
else:
assert (
training_parameters.cnn_parameters is None
), "Extra CNN parameters for non-image input"
trainer_params = DiscreteActionModelParameters(
actions=env.actions,
rl=rl_parameters,
training=training_parameters,
rainbow=rainbow_parameters,
)
trainer = create_dqn_trainer_from_params(
trainer_params, env.normalization, use_gpu
)
elif model_type == ModelType.PYTORCH_PARAMETRIC_DQN.value:
training_parameters = params["training"]
if isinstance(training_parameters, dict):
training_parameters = TrainingParameters(**training_parameters)
rainbow_parameters = params["rainbow"]
if isinstance(rainbow_parameters, dict):
rainbow_parameters = RainbowDQNParameters(**rainbow_parameters)
if env.img:
assert (
training_parameters.cnn_parameters is not None
), "Missing CNN parameters for image input"
training_parameters.cnn_parameters.conv_dims[0] = env.num_input_channels
else:
assert (
training_parameters.cnn_parameters is None
), "Extra CNN parameters for non-image input"
trainer_params = ContinuousActionModelParameters(
rl=rl_parameters, training=training_parameters, rainbow=rainbow_parameters
)
trainer = create_parametric_dqn_trainer_from_params(
trainer_params, env.normalization, env.normalization_action, use_gpu
)
elif model_type == ModelType.TD3.value:
trainer_params = TD3ModelParameters(
rl=rl_parameters,
training=TD3TrainingParameters(
minibatch_size=params["td3_training"]["minibatch_size"],
q_network_optimizer=OptimizerParameters(
**params["td3_training"]["q_network_optimizer"]
),
actor_network_optimizer=OptimizerParameters(
**params["td3_training"]["actor_network_optimizer"]
),
use_2_q_functions=params["td3_training"]["use_2_q_functions"],
exploration_noise=params["td3_training"]["exploration_noise"],
initial_exploration_ts=params["td3_training"]["initial_exploration_ts"],
target_policy_smoothing=params["td3_training"][
"target_policy_smoothing"
],
noise_clip=params["td3_training"]["noise_clip"],
delayed_policy_update=params["td3_training"]["delayed_policy_update"],
),
q_network=FeedForwardParameters(**params["td3_q_training"]),
actor_network=FeedForwardParameters(**params["td3_actor_training"]),
)
trainer = get_td3_trainer(env, trainer_params, use_gpu)
elif model_type == ModelType.SOFT_ACTOR_CRITIC.value:
value_network = None
value_network_optimizer = None
alpha_optimizer = None
if params["sac_training"]["use_value_network"]:
value_network = FeedForwardParameters(**params["sac_value_training"])
value_network_optimizer = OptimizerParameters(
**params["sac_training"]["value_network_optimizer"]
)
if "alpha_optimizer" in params["sac_training"]:
alpha_optimizer = OptimizerParameters(
**params["sac_training"]["alpha_optimizer"]
)
entropy_temperature = params["sac_training"].get("entropy_temperature", None)
target_entropy = params["sac_training"].get("target_entropy", None)
trainer_params = SACModelParameters(
rl=rl_parameters,
training=SACTrainingParameters(
minibatch_size=params["sac_training"]["minibatch_size"],
use_2_q_functions=params["sac_training"]["use_2_q_functions"],
use_value_network=params["sac_training"]["use_value_network"],
q_network_optimizer=OptimizerParameters(
**params["sac_training"]["q_network_optimizer"]
),
value_network_optimizer=value_network_optimizer,
actor_network_optimizer=OptimizerParameters(
**params["sac_training"]["actor_network_optimizer"]
),
entropy_temperature=entropy_temperature,
target_entropy=target_entropy,
alpha_optimizer=alpha_optimizer,
),
q_network=FeedForwardParameters(**params["sac_q_training"]),
value_network=value_network,
actor_network=FeedForwardParameters(**params["sac_actor_training"]),
)
trainer = get_sac_trainer(env, trainer_params, use_gpu)
else:
raise NotImplementedError("Model of type {} not supported".format(model_type))
return trainer
def train_network(params):
writer = None
if params["model_output_path"] is not None:
writer = SummaryWriter(
log_dir=os.path.join(
os.path.expanduser(params["model_output_path"]), "training_data"
)
)
logger.info("Running DQN workflow with params:")
logger.info(params)
action_names = np.array(params["actions"])
rl_parameters = RLParameters(**params["rl"])
training_parameters = TrainingParameters(**params["training"])
rainbow_parameters = RainbowDQNParameters(**params["rainbow"])
if params["in_training_cpe"] is not None:
in_training_cpe_parameters = InTrainingCPEParameters(
**params["in_training_cpe"]
)
else:
in_training_cpe_parameters = None
trainer_params = DiscreteActionModelParameters(
actions=params["actions"],
rl=rl_parameters,
training=training_parameters,
rainbow=rainbow_parameters,
in_training_cpe=in_training_cpe_parameters,
)
dataset = JSONDataset(
params["training_data_path"], batch_size=training_parameters.minibatch_size
)
state_normalization = read_norm_file(params["state_norm_data_path"])
num_batches = int(len(dataset) / training_parameters.minibatch_size)
logger.info(
"Read in batch data set {} of size {} examples. Data split "
"into {} batches of size {}.".format(
params["training_data_path"],
len(dataset),
num_batches,
training_parameters.minibatch_size,
)
)
trainer = DQNTrainer(trainer_params, state_normalization, params["use_gpu"])
trainer = update_model_for_warm_start(trainer)
preprocessor = Preprocessor(state_normalization, params["use_gpu"])
if trainer_params.in_training_cpe is not None:
evaluator = Evaluator(
trainer_params.actions,
10,
trainer_params.rl.gamma,
trainer,
trainer_params.in_training_cpe.mdp_sampled_rate,
)
else:
evaluator = Evaluator(
trainer_params.actions,
10,
trainer_params.rl.gamma,
trainer,
float(DEFAULT_NUM_SAMPLES_FOR_CPE) / len(dataset),
)
start_time = time.time()
for epoch in range(int(params["epochs"])):
for batch_idx in range(num_batches):
report_training_status(batch_idx, num_batches, epoch, int(params["epochs"]))
batch = dataset.read_batch(batch_idx)
tdp = preprocess_batch_for_training(preprocessor, batch, action_names)
trainer.train(tdp)
trainer.evaluate(
evaluator, tdp.actions, None, tdp.rewards, tdp.episode_values
)
evaluator.collect_discrete_action_samples(
mdp_ids=tdp.mdp_ids,
sequence_numbers=tdp.sequence_numbers.cpu().numpy(),
states=tdp.states.cpu().numpy(),
logged_actions=tdp.actions.cpu().numpy(),
logged_rewards=tdp.rewards.cpu().numpy(),
logged_propensities=tdp.propensities.cpu().numpy(),
logged_terminals=np.invert(
tdp.not_terminals.cpu().numpy().astype(np.bool)
),
)
cpe_start_time = time.time()
evaluator.recover_samples_to_be_unshuffled()
evaluator.score_cpe()
if writer is not None:
evaluator.log_to_tensorboard(writer, epoch)
evaluator.clear_collected_samples()
logger.info(
"CPE evaluation took {} seconds.".format(time.time() - cpe_start_time)
)
through_put = (len(dataset) * int(params["epochs"])) / (time.time() - start_time)
logger.info(
"Training finished. Processed ~{} examples / s.".format(round(through_put))
)
if writer is not None:
writer.close()
return export_trainer_and_predictor(trainer, params["model_output_path"])
def run_gym(
params,
score_bar,
gpu_id,
save_timesteps_to_dataset=None,
start_saving_from_episode=0,
batch_rl_file_path=None,
):
# Caffe2 core uses the min of caffe2_log_level and minloglevel
# to determine loglevel. See caffe2/caffe2/core/logging.cc for more info.
core.GlobalInit(["caffe2", "--caffe2_log_level=2", "--minloglevel=2"])
logger.info("Running gym with params")
logger.info(params)
rl_parameters = RLParameters(**params["rl"])
env_type = params["env"]
env = OpenAIGymEnvironment(
env_type,
rl_parameters.epsilon,
rl_parameters.softmax_policy,
params["max_replay_memory_size"],
rl_parameters.gamma,
)
model_type = params["model_type"]
c2_device = core.DeviceOption(
caffe2_pb2.CPU if gpu_id == USE_CPU else caffe2_pb2.CUDA, gpu_id)
use_gpu = gpu_id != USE_CPU
if model_type == ModelType.PYTORCH_DISCRETE_DQN.value:
training_settings = params["training"]
training_parameters = TrainingParameters(**training_settings)
if env.img:
assert (training_parameters.cnn_parameters
is not None), "Missing CNN parameters for image input"
training_parameters.cnn_parameters = CNNParameters(
**training_settings["cnn_parameters"])
training_parameters.cnn_parameters.conv_dims[
0] = env.num_input_channels
training_parameters.cnn_parameters.input_height = env.height
training_parameters.cnn_parameters.input_width = env.width
training_parameters.cnn_parameters.num_input_channels = (
env.num_input_channels)
else:
assert (training_parameters.cnn_parameters is
None), "Extra CNN parameters for non-image input"
trainer_params = DiscreteActionModelParameters(
actions=env.actions,
rl=rl_parameters,
training=training_parameters)
trainer = DQNTrainer(trainer_params, env.normalization, use_gpu)
elif model_type == ModelType.DISCRETE_ACTION.value:
with core.DeviceScope(c2_device):
training_settings = params["training"]
training_parameters = TrainingParameters(**training_settings)
if env.img:
assert (training_parameters.cnn_parameters
is not None), "Missing CNN parameters for image input"
training_parameters.cnn_parameters = CNNParameters(
**training_settings["cnn_parameters"])
training_parameters.cnn_parameters.conv_dims[
0] = env.num_input_channels
training_parameters.cnn_parameters.input_height = env.height
training_parameters.cnn_parameters.input_width = env.width
training_parameters.cnn_parameters.num_input_channels = (
env.num_input_channels)
else:
assert (training_parameters.cnn_parameters is
None), "Extra CNN parameters for non-image input"
trainer_params = DiscreteActionModelParameters(
actions=env.actions,
rl=rl_parameters,
training=training_parameters)
trainer = DiscreteActionTrainer(trainer_params, env.normalization)
elif model_type == ModelType.PYTORCH_PARAMETRIC_DQN.value:
training_settings = params["training"]
training_parameters = TrainingParameters(**training_settings)
if env.img:
assert (training_parameters.cnn_parameters
is not None), "Missing CNN parameters for image input"
training_parameters.cnn_parameters = CNNParameters(
**training_settings["cnn_parameters"])
training_parameters.cnn_parameters.conv_dims[
0] = env.num_input_channels
else:
assert (training_parameters.cnn_parameters is
None), "Extra CNN parameters for non-image input"
trainer_params = ContinuousActionModelParameters(
rl=rl_parameters,
training=training_parameters,
knn=KnnParameters(model_type="DQN"),
)
trainer = ParametricDQNTrainer(trainer_params, env.normalization,
env.normalization_action, use_gpu)
elif model_type == ModelType.PARAMETRIC_ACTION.value:
with core.DeviceScope(c2_device):
training_settings = params["training"]
training_parameters = TrainingParameters(**training_settings)
if env.img:
assert (training_parameters.cnn_parameters
is not None), "Missing CNN parameters for image input"
training_parameters.cnn_parameters = CNNParameters(
**training_settings["cnn_parameters"])
training_parameters.cnn_parameters.conv_dims[
0] = env.num_input_channels
else:
assert (training_parameters.cnn_parameters is
None), "Extra CNN parameters for non-image input"
trainer_params = ContinuousActionModelParameters(
rl=rl_parameters,
training=training_parameters,
knn=KnnParameters(model_type="DQN"),
)
trainer = ContinuousActionDQNTrainer(trainer_params,
env.normalization,
env.normalization_action)
elif model_type == ModelType.CONTINUOUS_ACTION.value:
training_settings = params["shared_training"]
actor_settings = params["actor_training"]
critic_settings = params["critic_training"]
trainer_params = DDPGModelParameters(
rl=rl_parameters,
shared_training=DDPGTrainingParameters(**training_settings),
actor_training=DDPGNetworkParameters(**actor_settings),
critic_training=DDPGNetworkParameters(**critic_settings),
)
action_range_low = env.action_space.low.astype(np.float32)
action_range_high = env.action_space.high.astype(np.float32)
trainer = DDPGTrainer(
trainer_params,
env.normalization,
env.normalization_action,
torch.from_numpy(action_range_low).unsqueeze(dim=0),
torch.from_numpy(action_range_high).unsqueeze(dim=0),
use_gpu,
)
else:
raise NotImplementedError(
"Model of type {} not supported".format(model_type))
return run(
c2_device,
env,
model_type,
trainer,
"{} test run".format(env_type),
score_bar,
**params["run_details"],
save_timesteps_to_dataset=save_timesteps_to_dataset,
start_saving_from_episode=start_saving_from_episode,
batch_rl_file_path=batch_rl_file_path,
)
def single_process_main(gpu_index, *args):
params = args[0]
# Set minibatch size based on # of devices being used to train
params["training"]["minibatch_size"] *= minibatch_size_multiplier(
params["use_gpu"], params["use_all_avail_gpus"]
)
action_names = params["actions"]
rl_parameters = RLParameters(**params["rl"])
training_parameters = TrainingParameters(**params["training"])
rainbow_parameters = RainbowDQNParameters(**params["rainbow"])
model_params = DiscreteActionModelParameters(
actions=action_names,
rl=rl_parameters,
training=training_parameters,
rainbow=rainbow_parameters,
)
state_normalization = BaseWorkflow.read_norm_file(params["state_norm_data_path"])
writer = SummaryWriter(log_dir=params["model_output_path"])
logger.info("TensorBoard logging location is: {}".format(writer.log_dir))
if params["use_all_avail_gpus"]:
BaseWorkflow.init_multiprocessing(
int(params["num_processes_per_node"]),
int(params["num_nodes"]),
int(params["node_index"]),
gpu_index,
params["init_method"],
)
workflow = DqnWorkflow(
model_params,
state_normalization,
params["use_gpu"],
params["use_all_avail_gpus"],
)
sorted_features, _ = sort_features_by_normalization(state_normalization)
preprocess_handler = DiscreteDqnPreprocessHandler(
action_names, PandasSparseToDenseProcessor(sorted_features)
)
train_dataset = JSONDatasetReader(
params["training_data_path"],
batch_size=training_parameters.minibatch_size,
preprocess_handler=preprocess_handler,
)
eval_dataset = JSONDatasetReader(
params["eval_data_path"],
batch_size=training_parameters.minibatch_size,
preprocess_handler=preprocess_handler,
)
with summary_writer_context(writer):
workflow.train_network(train_dataset, eval_dataset, int(params["epochs"]))
exporter = DQNExporter(
workflow.trainer.q_network,
PredictorFeatureExtractor(state_normalization_parameters=state_normalization),
DiscreteActionOutputTransformer(model_params.actions),
)
if int(params["node_index"]) == 0 and gpu_index == 0:
export_trainer_and_predictor(
workflow.trainer, params["model_output_path"], exporter=exporter
) # noqa
def create_park_trainer(model_type, params, rl_parameters, use_gpu, env):
if model_type == ModelType.PYTORCH_DISCRETE_DQN.value:
training_parameters = params["training"]
if isinstance(training_parameters, dict):
training_parameters = TrainingParameters(**training_parameters)
rainbow_parameters = params["rainbow"]
if isinstance(rainbow_parameters, dict):
rainbow_parameters = RainbowDQNParameters(**rainbow_parameters)
if env.img:
assert (training_parameters.cnn_parameters
is not None), "Missing CNN parameters for image input"
if isinstance(training_parameters.cnn_parameters, dict):
training_parameters.cnn_parameters = CNNParameters(
**training_parameters.cnn_parameters)
training_parameters.cnn_parameters.conv_dims[
0] = env.num_input_channels
training_parameters.cnn_parameters.input_height = env.height
training_parameters.cnn_parameters.input_width = env.width
training_parameters.cnn_parameters.num_input_channels = (
env.num_input_channels)
else:
assert (training_parameters.cnn_parameters is
None), "Extra CNN parameters for non-image input"
trainer_params = DiscreteActionModelParameters(
actions=env.actions,
rl=rl_parameters,
training=training_parameters,
rainbow=rainbow_parameters,
)
trainer = create_park_dqn_trainer_from_params(
model=trainer_params,
normalization_parameters=env.normalization,
use_gpu=use_gpu,
env=env.env)
elif model_type == ModelType.PYTORCH_PARAMETRIC_DQN.value:
training_parameters = params["training"]
if isinstance(training_parameters, dict):
training_parameters = TrainingParameters(**training_parameters)
rainbow_parameters = params["rainbow"]
if isinstance(rainbow_parameters, dict):
rainbow_parameters = RainbowDQNParameters(**rainbow_parameters)
if env.img:
assert (training_parameters.cnn_parameters
is not None), "Missing CNN parameters for image input"
training_parameters.cnn_parameters.conv_dims[
0] = env.num_input_channels
else:
assert (training_parameters.cnn_parameters is
None), "Extra CNN parameters for non-image input"
trainer_params = ContinuousActionModelParameters(
rl=rl_parameters,
training=training_parameters,
rainbow=rainbow_parameters)
trainer = create_parametric_dqn_trainer_from_params(
trainer_params,
env.normalization,
env.normalization_action,
use_gpu,
env=env.env)
elif model_type == ModelType.CONTINUOUS_ACTION.value:
training_parameters = params["shared_training"]
if isinstance(training_parameters, dict):
training_parameters = DDPGTrainingParameters(**training_parameters)
actor_parameters = params["actor_training"]
if isinstance(actor_parameters, dict):
actor_parameters = DDPGNetworkParameters(**actor_parameters)
critic_parameters = params["critic_training"]
if isinstance(critic_parameters, dict):
critic_parameters = DDPGNetworkParameters(**critic_parameters)
trainer_params = DDPGModelParameters(
rl=rl_parameters,
shared_training=training_parameters,
actor_training=actor_parameters,
critic_training=critic_parameters,
)
action_range_low = env.action_space.low.astype(np.float32)
action_range_high = env.action_space.high.astype(np.float32)
state_dim = get_num_output_features(env.normalization)
action_dim = get_num_output_features(env.normalization_action)
# Build Actor Network
actor_network = ActorNetModel(
layers=([state_dim] + trainer_params.actor_training.layers[1:-1] +
[action_dim]),
activations=trainer_params.actor_training.activations,
fl_init=trainer_params.shared_training.final_layer_init,
state_dim=state_dim,
action_dim=action_dim,
use_gpu=use_gpu,
use_all_avail_gpus=False,
)
# Build Critic Network
critic_network = CriticNetModel(
# Ensure dims match input state and scalar output
layers=[state_dim] + \
trainer_params.critic_training.layers[1:-1] + [1],
activations=trainer_params.critic_training.activations,
fl_init=trainer_params.shared_training.final_layer_init,
state_dim=state_dim,
action_dim=action_dim,
use_gpu=use_gpu,
use_all_avail_gpus=False,
)
trainer = DDPGTrainer(
actor_network,
critic_network,
trainer_params,
env.normalization,
env.normalization_action,
torch.from_numpy(action_range_low).unsqueeze(dim=0),
torch.from_numpy(action_range_high).unsqueeze(dim=0),
use_gpu,
)
elif model_type == ModelType.SOFT_ACTOR_CRITIC.value:
value_network = None
value_network_optimizer = None
if params["sac_training"]["use_value_network"]:
value_network = FeedForwardParameters(
**params["sac_value_training"])
value_network_optimizer = OptimizerParameters(
**params["sac_training"]["value_network_optimizer"])
trainer_params = SACModelParameters(
rl=rl_parameters,
training=SACTrainingParameters(
minibatch_size=params["sac_training"]["minibatch_size"],
use_2_q_functions=params["sac_training"]["use_2_q_functions"],
use_value_network=params["sac_training"]["use_value_network"],
q_network_optimizer=OptimizerParameters(
**params["sac_training"]["q_network_optimizer"]),
value_network_optimizer=value_network_optimizer,
actor_network_optimizer=OptimizerParameters(
**params["sac_training"]["actor_network_optimizer"]),
entropy_temperature=params["sac_training"]
["entropy_temperature"],
),
q_network=FeedForwardParameters(**params["sac_q_training"]),
value_network=value_network,
actor_network=FeedForwardParameters(
**params["sac_actor_training"]),
)
trainer = horizon_runner.get_sac_trainer(env, trainer_params, use_gpu)
else:
raise NotImplementedError(
"Model of type {} not supported".format(model_type))
return trainer
def create_trainer(model_type, params, rl_parameters, use_gpu, env):
c2_device = core.DeviceOption(caffe2_pb2.CUDA if use_gpu else caffe2_pb2.CPU)
if model_type == ModelType.PYTORCH_DISCRETE_DQN.value:
training_parameters = params["training"]
if isinstance(training_parameters, dict):
training_parameters = TrainingParameters(**training_parameters)
rainbow_parameters = params["rainbow"]
if isinstance(rainbow_parameters, dict):
rainbow_parameters = RainbowDQNParameters(**rainbow_parameters)
if env.img:
assert (
training_parameters.cnn_parameters is not None
), "Missing CNN parameters for image input"
training_parameters.cnn_parameters.conv_dims[0] = env.num_input_channels
training_parameters.cnn_parameters.input_height = env.height
training_parameters.cnn_parameters.input_width = env.width
training_parameters.cnn_parameters.num_input_channels = (
env.num_input_channels
)
else:
assert (
training_parameters.cnn_parameters is None
), "Extra CNN parameters for non-image input"
trainer_params = DiscreteActionModelParameters(
actions=env.actions,
rl=rl_parameters,
training=training_parameters,
rainbow=rainbow_parameters,
)
trainer = DQNTrainer(trainer_params, env.normalization, use_gpu)
elif model_type == ModelType.DISCRETE_ACTION.value:
with core.DeviceScope(c2_device):
training_parameters = params["training"]
if isinstance(training_parameters, dict):
training_parameters = TrainingParameters(**training_parameters)
if env.img:
assert (
training_parameters.cnn_parameters is not None
), "Missing CNN parameters for image input"
training_parameters.cnn_parameters.conv_dims[0] = env.num_input_channels
training_parameters.cnn_parameters.input_height = env.height
training_parameters.cnn_parameters.input_width = env.width
training_parameters.cnn_parameters.num_input_channels = (
env.num_input_channels
)
else:
assert (
training_parameters.cnn_parameters is None
), "Extra CNN parameters for non-image input"
trainer_params = DiscreteActionModelParameters(
actions=env.actions, rl=rl_parameters, training=training_parameters
)
trainer = DiscreteActionTrainer(trainer_params, env.normalization)
elif model_type == ModelType.PYTORCH_PARAMETRIC_DQN.value:
training_parameters = params["training"]
if isinstance(training_parameters, dict):
training_parameters = TrainingParameters(**training_parameters)
rainbow_parameters = params["rainbow"]
if isinstance(rainbow_parameters, dict):
rainbow_parameters = RainbowDQNParameters(**rainbow_parameters)
if env.img:
assert (
training_parameters.cnn_parameters is not None
), "Missing CNN parameters for image input"
training_parameters.cnn_parameters.conv_dims[0] = env.num_input_channels
else:
assert (
training_parameters.cnn_parameters is None
), "Extra CNN parameters for non-image input"
trainer_params = ContinuousActionModelParameters(
rl=rl_parameters,
training=training_parameters,
knn=KnnParameters(model_type="DQN"),
rainbow=rainbow_parameters,
)
trainer = ParametricDQNTrainer(
trainer_params, env.normalization, env.normalization_action, use_gpu
)
elif model_type == ModelType.PARAMETRIC_ACTION.value:
with core.DeviceScope(c2_device):
training_parameters = params["training"]
if isinstance(training_parameters, dict):
training_parameters = TrainingParameters(**training_parameters)
if env.img:
assert (
training_parameters.cnn_parameters is not None
), "Missing CNN parameters for image input"
training_parameters.cnn_parameters.conv_dims[0] = env.num_input_channels
else:
assert (
training_parameters.cnn_parameters is None
), "Extra CNN parameters for non-image input"
trainer_params = ContinuousActionModelParameters(
rl=rl_parameters,
training=training_parameters,
knn=KnnParameters(model_type="DQN"),
)
trainer = ContinuousActionDQNTrainer(
trainer_params, env.normalization, env.normalization_action
)
elif model_type == ModelType.CONTINUOUS_ACTION.value:
training_parameters = params["shared_training"]
if isinstance(training_parameters, dict):
training_parameters = DDPGTrainingParameters(**training_parameters)
actor_parameters = params["actor_training"]
if isinstance(actor_parameters, dict):
actor_parameters = DDPGNetworkParameters(**actor_parameters)
critic_parameters = params["critic_training"]
if isinstance(critic_parameters, dict):
critic_parameters = DDPGNetworkParameters(**critic_parameters)
trainer_params = DDPGModelParameters(
rl=rl_parameters,
shared_training=training_parameters,
actor_training=actor_parameters,
critic_training=critic_parameters,
)
action_range_low = env.action_space.low.astype(np.float32)
action_range_high = env.action_space.high.astype(np.float32)
trainer = DDPGTrainer(
trainer_params,
env.normalization,
env.normalization_action,
torch.from_numpy(action_range_low).unsqueeze(dim=0),
torch.from_numpy(action_range_high).unsqueeze(dim=0),
use_gpu,
)
else:
raise NotImplementedError("Model of type {} not supported".format(model_type))
return trainer
