def get_parameters(self, env, action_dim=4, k=2):
# ONNX does't export `normalize`
return KNNDQNModelParameters(
rl=RLParameters(
gamma=DISCOUNT,
target_update_rate=0.5,
reward_burnin=100,
maxq_learning=False,
),
shared_training=DDPGTrainingParameters(
minibatch_size=self.minibatch_size,
final_layer_init=0.003,
optimizer="ADAM",
),
actor_training=DDPGNetworkParameters(
layers=[-1, 64, 32, -1],
activations=["relu", "relu", "tanh"],
learning_rate=0.1,
),
critic_training=DDPGNetworkParameters(
layers=[-1, 64, 32, -1],
activations=["relu", "relu", "linear"],
learning_rate=0.1,
l2_decay=0.999,
),
num_actions=len(env.ACTIONS),
action_dim=action_dim,
k=k,
)
def get_ddpg_parameters(self):
return DDPGModelParameters(
rl=RLParameters(
gamma=DISCOUNT,
target_update_rate=0.5,
reward_burnin=10,
maxq_learning=False,
),
shared_training=DDPGTrainingParameters(
minibatch_size=self.minibatch_size,
final_layer_init=0.003,
optimizer="ADAM",
),
actor_training=DDPGNetworkParameters(
layers=[-1, 400, 300, -1],
activations=["relu", "relu", "tanh"],
learning_rate=0.0001,
),
critic_training=DDPGNetworkParameters(
layers=[-1, 400, 300, -1],
activations=["relu", "relu", "tanh"],
learning_rate=0.001,
l2_decay=0.01,
),
)
def main(params):
# Set minibatch size based on # of devices being used to train
params["shared_training"]["minibatch_size"] *= minibatch_size_multiplier(
params["use_gpu"], params["use_all_avail_gpus"])
rl_parameters = RLParameters(**params["rl"])
training_parameters = DDPGTrainingParameters(**params["shared_training"])
actor_parameters = DDPGNetworkParameters(**params["actor_training"])
critic_parameters = DDPGNetworkParameters(**params["critic_training"])
model_params = DDPGModelParameters(
rl=rl_parameters,
shared_training=training_parameters,
actor_training=actor_parameters,
critic_training=critic_parameters,
)
state_normalization = BaseWorkflow.read_norm_file(
params["state_norm_data_path"])
action_normalization = BaseWorkflow.read_norm_file(
params["action_norm_data_path"])
writer = SummaryWriter(log_dir=params["model_output_path"])
logger.info("TensorBoard logging location is: {}".format(writer.log_dir))
preprocess_handler = ContinuousPreprocessHandler(
Preprocessor(state_normalization, False),
Preprocessor(action_normalization, False),
PandasSparseToDenseProcessor(),
)
workflow = ContinuousWorkflow(
model_params,
preprocess_handler,
state_normalization,
action_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"]))
return export_trainer_and_predictor(
workflow.trainer,
params["model_output_path"],
exporter=_get_actor_exporter(
trainer=workflow.trainer,
state_normalization=state_normalization,
action_normalization=action_normalization,
),
) # noqa
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 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):
logger.info("Running Parametric DQN workflow with params:")
logger.info(params)
rl_parameters = RLParameters(**params["rl"])
training_parameters = DDPGTrainingParameters(**params["shared_training"])
actor_parameters = DDPGNetworkParameters(**params["actor_training"])
critic_parameters = DDPGNetworkParameters(**params["critic_training"])
trainer_params = DDPGModelParameters(
rl=rl_parameters,
shared_training=training_parameters,
actor_training=actor_parameters,
critic_training=critic_parameters,
)
dataset = JSONDataset(params["training_data_path"],
batch_size=training_parameters.minibatch_size)
state_normalization = read_norm_file(params["state_norm_data_path"])
action_normalization = read_norm_file(params["action_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,
))
min_action_range_tensor_serving, max_action_range_tensor_serving = construct_action_scale_tensor(
action_normalization, trainer_params.action_rescale_map)
trainer = DDPGTrainer(
trainer_params,
state_normalization,
action_normalization,
min_action_range_tensor_serving,
max_action_range_tensor_serving,
params["use_gpu"],
)
trainer = update_model_for_warm_start(trainer)
state_preprocessor = Preprocessor(state_normalization, params["use_gpu"])
action_preprocessor = Preprocessor(action_normalization, params["use_gpu"])
start_time = time.time()
for epoch in range(params["epochs"]):
for batch_idx in range(num_batches):
report_training_status(batch_idx, num_batches, epoch,
params["epochs"])
batch = dataset.read_batch(batch_idx)
tdp = preprocess_batch_for_training(
state_preprocessor,
batch,
action_preprocessor=action_preprocessor)
trainer.train(tdp)
through_put = (len(dataset) * params["epochs"]) / (time.time() -
start_time)
logger.info("Training finished. Processed ~{} examples / s.".format(
round(through_put)))
return export_trainer_and_predictor(trainer, params["model_output_path"])
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
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 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 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 train_network(params):
logger.info("Running Parametric DQN workflow with params:")
logger.info(params)
# Set minibatch size based on # of devices being used to train
params["shared_training"]["minibatch_size"] *= minibatch_size_multiplier(
params["use_gpu"], params["use_all_avail_gpus"])
rl_parameters = RLParameters(**params["rl"])
training_parameters = DDPGTrainingParameters(**params["shared_training"])
actor_parameters = DDPGNetworkParameters(**params["actor_training"])
critic_parameters = DDPGNetworkParameters(**params["critic_training"])
trainer_params = DDPGModelParameters(
rl=rl_parameters,
shared_training=training_parameters,
actor_training=actor_parameters,
critic_training=critic_parameters,
)
dataset = JSONDataset(params["training_data_path"],
batch_size=training_parameters.minibatch_size)
state_normalization = read_norm_file(params["state_norm_data_path"])
action_normalization = read_norm_file(params["action_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,
))
min_action_range_tensor_serving, max_action_range_tensor_serving = construct_action_scale_tensor(
action_normalization, trainer_params.action_rescale_map)
trainer = DDPGTrainer(
trainer_params,
state_normalization,
action_normalization,
min_action_range_tensor_serving,
max_action_range_tensor_serving,
use_gpu=params["use_gpu"],
use_all_avail_gpus=params["use_all_avail_gpus"],
)
trainer = update_model_for_warm_start(trainer)
state_preprocessor = Preprocessor(state_normalization, False)
action_preprocessor = Preprocessor(action_normalization, False)
start_time = time.time()
for epoch in range(params["epochs"]):
dataset.reset_iterator()
batch_idx = -1
while True:
batch_idx += 1
report_training_status(batch_idx, num_batches, epoch,
params["epochs"])
batch = dataset.read_batch()
if batch is None:
break
tdp = preprocess_batch_for_training(
state_preprocessor,
batch,
action_preprocessor=action_preprocessor)
tdp.set_type(trainer.dtype)
trainer.train(tdp)
through_put = (len(dataset) * params["epochs"]) / (time.time() -
start_time)
logger.info("Training finished. Processed ~{} examples / s.".format(
round(through_put)))
return export_trainer_and_predictor(trainer, params["model_output_path"])
