def validate_config(self, config: AlgorithmConfigDict) -> None:
# First check, whether old `timesteps_per_iteration` is used. If so
# convert right away as for CQL, we must measure in training timesteps,
# never sampling timesteps (CQL does not sample).
if config.get("timesteps_per_iteration",
DEPRECATED_VALUE) != DEPRECATED_VALUE:
deprecation_warning(
old="timesteps_per_iteration",
new="min_train_timesteps_per_iteration",
error=False,
)
config["min_train_timesteps_per_iteration"] = config[
"timesteps_per_iteration"]
config["timesteps_per_iteration"] = DEPRECATED_VALUE
# Call super's validation method.
super().validate_config(config)
if config["num_gpus"] > 1:
raise ValueError("`num_gpus` > 1 not yet supported for CQL!")
# CQL-torch performs the optimizer steps inside the loss function.
# Using the multi-GPU optimizer will therefore not work (see multi-GPU
# check above) and we must use the simple optimizer for now.
if config["simple_optimizer"] is not True and config[
"framework"] == "torch":
config["simple_optimizer"] = True
if config["framework"] in ["tf", "tf2", "tfe"] and tfp is None:
logger.warning(
"You need `tensorflow_probability` in order to run CQL! "
"Install it via `pip install tensorflow_probability`. Your "
f"tf.__version__={tf.__version__ if tf else None}."
"Trying to import tfp results in the following error:")
try_import_tfp(error=True)
def validate_config(self, config: AlgorithmConfigDict) -> None:
# Call super's validation method.
super().validate_config(config)
if config["use_state_preprocessor"] != DEPRECATED_VALUE:
deprecation_warning(old="config['use_state_preprocessor']",
error=False)
config["use_state_preprocessor"] = DEPRECATED_VALUE
if config.get("policy_model", DEPRECATED_VALUE) != DEPRECATED_VALUE:
deprecation_warning(
old="config['policy_model']",
new="config['policy_model_config']",
error=False,
)
config["policy_model_config"] = config["policy_model"]
if config.get("Q_model", DEPRECATED_VALUE) != DEPRECATED_VALUE:
deprecation_warning(
old="config['Q_model']",
new="config['q_model_config']",
error=False,
)
config["q_model_config"] = config["Q_model"]
if config["grad_clip"] is not None and config["grad_clip"] <= 0.0:
raise ValueError("`grad_clip` value must be > 0.0!")
if config["framework"] in ["tf", "tf2", "tfe"] and tfp is None:
logger.warning(
"You need `tensorflow_probability` in order to run SAC! "
"Install it via `pip install tensorflow_probability`. Your "
f"tf.__version__={tf.__version__ if tf else None}."
"Trying to import tfp results in the following error:")
try_import_tfp(error=True)
def validate_config(config: TrainerConfigDict):
if config["num_gpus"] > 1:
raise ValueError("`num_gpus` > 1 not yet supported for CQL!")
# CQL-torch performs the optimizer steps inside the loss function.
# Using the multi-GPU optimizer will therefore not work (see multi-GPU
# check above) and we must use the simple optimizer for now.
if config["simple_optimizer"] is not True and \
config["framework"] == "torch":
config["simple_optimizer"] = True
if config["framework"] in ["tf", "tf2", "tfe"] and tfp is None:
logger.warning(
"You need `tensorflow_probability` in order to run CQL! "
"Install it via `pip install tensorflow_probability`. Your "
f"tf.__version__={tf.__version__ if tf else None}."
"Trying to import tfp results in the following error:")
try_import_tfp(error=True)
def validate_config(config: TrainerConfigDict) -> None:
"""Validates the Trainer's config dict.
Args:
config (TrainerConfigDict): The Trainer's config to check.
Raises:
ValueError: In case something is wrong with the config.
"""
if config["use_state_preprocessor"] != DEPRECATED_VALUE:
deprecation_warning(old="config['use_state_preprocessor']",
error=False)
config["use_state_preprocessor"] = DEPRECATED_VALUE
if config["grad_clip"] is not None and config["grad_clip"] <= 0.0:
raise ValueError("`grad_clip` value must be > 0.0!")
if config["framework"] in ["tf", "tf2", "tfe"] and tfp is None:
logger.warning(
"You need `tensorflow_probability` in order to run SAC! "
"Install it via `pip install tensorflow_probability`. Your "
f"tf.__version__={tf.__version__ if tf else None}."
"Trying to import tfp results in the following error:")
try_import_tfp(error=True)
from math import log
import numpy as np
import functools
import tree
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.utils import MIN_LOG_NN_OUTPUT, MAX_LOG_NN_OUTPUT, \
SMALL_NUMBER
from ray.rllib.utils.annotations import override, DeveloperAPI
from ray.rllib.utils.framework import try_import_tf, try_import_tfp
from ray.rllib.utils.spaces.space_utils import get_base_struct_from_space
from ray.rllib.utils.typing import TensorType, List
tf1, tf, tfv = try_import_tf()
tfp = try_import_tfp()
@DeveloperAPI
class TFActionDistribution(ActionDistribution):
"""TF-specific extensions for building action distributions."""
@override(ActionDistribution)
def __init__(self, inputs: List[TensorType], model: ModelV2):
super().__init__(inputs, model)
self.sample_op = self._build_sample_op()
self.sampled_action_logp_op = self.logp(self.sample_op)
@DeveloperAPI
def _build_sample_op(self) -> TensorType:
"""Implement this instead of sample(), to enable op reuse.
