"""
Asynchronous Proximal Policy Optimization (APPO)
================================================
This file defines the distributed Algorithm class for the asynchronous version
of proximal policy optimization (APPO).
See `appo_[tf|torch]_policy.py` for the definition of the policy loss.
Detailed documentation:
https://docs.ray.io/en/master/rllib-algorithms.html#appo
"""
from typing import Optional, Type
import logging
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
from ray.rllib.algorithms.impala.impala import Impala, ImpalaConfig
from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
from ray.rllib.policy.policy import Policy
from ray.rllib.utils.annotations import override
from ray.rllib.utils.metrics import (
LAST_TARGET_UPDATE_TS,
NUM_AGENT_STEPS_SAMPLED,
NUM_ENV_STEPS_SAMPLED,
NUM_TARGET_UPDATES,
)
from ray.rllib.utils.metrics import ALL_MODULES, LEARNER_STATS_KEY
from ray.rllib.utils.typing import (
ResultDict,
)
logger = logging.getLogger(__name__)
LEARNER_RESULTS_KL_KEY = "mean_kl_loss"
LEARNER_RESULTS_CURR_KL_COEFF_KEY = "curr_kl_coeff"
OLD_ACTION_DIST_KEY = "old_action_dist"
OLD_ACTION_DIST_LOGITS_KEY = "old_action_dist_logits"
[docs]class APPOConfig(ImpalaConfig):
"""Defines a configuration class from which an APPO Algorithm can be built.
.. testcode::
from ray.rllib.algorithms.appo import APPOConfig
config = APPOConfig().training(lr=0.01, grad_clip=30.0, train_batch_size=50)
config = config.resources(num_gpus=0)
config = config.rollouts(num_rollout_workers=1)
config = config.environment("CartPole-v1")
# Build an Algorithm object from the config and run 1 training iteration.
algo = config.build()
algo.train()
del algo
.. testcode::
from ray.rllib.algorithms.appo import APPOConfig
from ray import air
from ray import tune
config = APPOConfig()
# Update the config object.
config = config.training(lr=tune.grid_search([0.001,]))
# Set the config object's env.
config = config.environment(env="CartPole-v1")
# Use to_dict() to get the old-style python config dict
# when running with tune.
tune.Tuner(
"APPO",
run_config=air.RunConfig(stop={"training_iteration": 1},
verbose=0),
param_space=config.to_dict(),
).fit()
.. testoutput::
:hide:
...
"""
def __init__(self, algo_class=None):
"""Initializes a APPOConfig instance."""
super().__init__(algo_class=algo_class or APPO)
# fmt: off
# __sphinx_doc_begin__
# APPO specific settings:
self.vtrace = True
self.use_critic = True
self.use_gae = True
self.lambda_ = 1.0
self.clip_param = 0.4
self.use_kl_loss = False
self.kl_coeff = 1.0
self.kl_target = 0.01
# Override some of ImpalaConfig's default values with APPO-specific values.
self.num_rollout_workers = 2
self.rollout_fragment_length = 50
self.train_batch_size = 500
self.min_time_s_per_iteration = 10
self.num_gpus = 0
self.num_multi_gpu_tower_stacks = 1
self.minibatch_buffer_size = 1
self.num_sgd_iter = 1
self.target_update_frequency = 1
self.replay_proportion = 0.0
self.replay_buffer_num_slots = 100
self.learner_queue_size = 16
self.learner_queue_timeout = 300
self.max_sample_requests_in_flight_per_worker = 2
self.broadcast_interval = 1
self.grad_clip = 40.0
# Note: Only when using _enable_new_api_stack=True can the clipping mode be
# configured by the user. On the old API stack, RLlib will always clip by
# global_norm, no matter the value of `grad_clip_by`.
self.grad_clip_by = "global_norm"
self.opt_type = "adam"
self.lr = 0.0005
self.lr_schedule = None
self.decay = 0.99
self.momentum = 0.0
self.epsilon = 0.1
self.vf_loss_coeff = 0.5
self.entropy_coeff = 0.01
self.entropy_coeff_schedule = None
self.tau = 1.0
self.exploration_config = {
# The Exploration class to use. In the simplest case, this is the name
# (str) of any class present in the `rllib.utils.exploration` package.
# You can also provide the python class directly or the full location
# of your class (e.g. "ray.rllib.utils.exploration.epsilon_greedy.
# EpsilonGreedy").
"type": "StochasticSampling",
# Add constructor kwargs here (if any).
}
# __sphinx_doc_end__
# fmt: on
[docs] @override(ImpalaConfig)
def training(
self,
*,
vtrace: Optional[bool] = NotProvided,
use_critic: Optional[bool] = NotProvided,
use_gae: Optional[bool] = NotProvided,
lambda_: Optional[float] = NotProvided,
clip_param: Optional[float] = NotProvided,
use_kl_loss: Optional[bool] = NotProvided,
kl_coeff: Optional[float] = NotProvided,
kl_target: Optional[float] = NotProvided,
tau: Optional[float] = NotProvided,
target_update_frequency: Optional[int] = NotProvided,
**kwargs,
) -> "APPOConfig":
"""Sets the training related configuration.
Args:
vtrace: Whether to use V-trace weighted advantages. If false, PPO GAE
advantages will be used instead.
use_critic: Should use a critic as a baseline (otherwise don't use value
baseline; required for using GAE). Only applies if vtrace=False.
use_gae: If true, use the Generalized Advantage Estimator (GAE)
with a value function, see https://arxiv.org/pdf/1506.02438.pdf.
Only applies if vtrace=False.
lambda_: GAE (lambda) parameter.
clip_param: PPO surrogate slipping parameter.
use_kl_loss: Whether to use the KL-term in the loss function.
kl_coeff: Coefficient for weighting the KL-loss term.
kl_target: Target term for the KL-term to reach (via adjusting the
`kl_coeff` automatically).
tau: The factor by which to update the target policy network towards
the current policy network. Can range between 0 and 1.
e.g. updated_param = tau * current_param + (1 - tau) * target_param
target_update_frequency: The frequency to update the target policy and
tune the kl loss coefficients that are used during training. After
setting this parameter, the algorithm waits for at least
`target_update_frequency * minibatch_size * num_sgd_iter` number of
samples to be trained on by the learner group before updating the target
networks and tuned the kl loss coefficients that are used during
training.
NOTE: This parameter is only applicable when using the Learner API
(_enable_new_api_stack=True).
Returns:
This updated AlgorithmConfig object.
"""
# Pass kwargs onto super's `training()` method.
super().training(**kwargs)
if vtrace is not NotProvided:
self.vtrace = vtrace
if use_critic is not NotProvided:
self.use_critic = use_critic
if use_gae is not NotProvided:
self.use_gae = use_gae
if lambda_ is not NotProvided:
self.lambda_ = lambda_
if clip_param is not NotProvided:
self.clip_param = clip_param
if use_kl_loss is not NotProvided:
self.use_kl_loss = use_kl_loss
if kl_coeff is not NotProvided:
self.kl_coeff = kl_coeff
if kl_target is not NotProvided:
self.kl_target = kl_target
if tau is not NotProvided:
self.tau = tau
if target_update_frequency is not NotProvided:
self.target_update_frequency = target_update_frequency
return self
@override(ImpalaConfig)
def get_default_learner_class(self):
if self.framework_str == "torch":
from ray.rllib.algorithms.appo.torch.appo_torch_learner import (
APPOTorchLearner,
)
return APPOTorchLearner
elif self.framework_str == "tf2":
from ray.rllib.algorithms.appo.tf.appo_tf_learner import APPOTfLearner
return APPOTfLearner
else:
raise ValueError(
f"The framework {self.framework_str} is not supported. "
"Use either 'torch' or 'tf2'."
)
@override(ImpalaConfig)
def get_default_rl_module_spec(self) -> SingleAgentRLModuleSpec:
if self.framework_str == "torch":
from ray.rllib.algorithms.appo.torch.appo_torch_rl_module import (
APPOTorchRLModule as RLModule,
)
elif self.framework_str == "tf2":
from ray.rllib.algorithms.appo.tf.appo_tf_rl_module import (
APPOTfRLModule as RLModule,
)
else:
raise ValueError(
f"The framework {self.framework_str} is not supported. "
"Use either 'torch' or 'tf2'."
)
from ray.rllib.algorithms.appo.appo_catalog import APPOCatalog
return SingleAgentRLModuleSpec(module_class=RLModule, catalog_class=APPOCatalog)
class APPO(Impala):
def __init__(self, config, *args, **kwargs):
"""Initializes an APPO instance."""
super().__init__(config, *args, **kwargs)
# After init: Initialize target net.
# TODO(avnishn): Does this need to happen in __init__? I think we can move it
# to setup()
if not self.config._enable_new_api_stack:
self.workers.local_worker().foreach_policy_to_train(
lambda p, _: p.update_target()
)
def after_train_step(self, train_results: ResultDict) -> None:
"""Updates the target network and the KL coefficient for the APPO-loss.
This method is called from within the `training_step` method after each train
update.
The target network update frequency is calculated automatically by the product
of `num_sgd_iter` setting (usually 1 for APPO) and `minibatch_buffer_size`.
Args:
train_results: The results dict collected during the most recent
training step.
"""
if self.config._enable_new_api_stack:
if NUM_TARGET_UPDATES in train_results:
self._counters[NUM_TARGET_UPDATES] += train_results[NUM_TARGET_UPDATES]
self._counters[LAST_TARGET_UPDATE_TS] = train_results[
LAST_TARGET_UPDATE_TS
]
else:
last_update = self._counters[LAST_TARGET_UPDATE_TS]
cur_ts = self._counters[
NUM_AGENT_STEPS_SAMPLED
if self.config.count_steps_by == "agent_steps"
else NUM_ENV_STEPS_SAMPLED
]
target_update_freq = (
self.config.num_sgd_iter * self.config.minibatch_buffer_size
)
if cur_ts - last_update > target_update_freq:
self._counters[NUM_TARGET_UPDATES] += 1
self._counters[LAST_TARGET_UPDATE_TS] = cur_ts
# Update our target network.
self.workers.local_worker().foreach_policy_to_train(
lambda p, _: p.update_target()
)
# Also update the KL-coefficient for the APPO loss, if necessary.
if self.config.use_kl_loss:
def update(pi, pi_id):
assert LEARNER_STATS_KEY not in train_results, (
"{} should be nested under policy id key".format(
LEARNER_STATS_KEY
),
train_results,
)
if pi_id in train_results:
kl = train_results[pi_id][LEARNER_STATS_KEY].get("kl")
assert kl is not None, (train_results, pi_id)
# Make the actual `Policy.update_kl()` call.
pi.update_kl(kl)
else:
logger.warning(
"No data for {}, not updating kl".format(pi_id)
)
# Update KL on all trainable policies within the local (trainer)
# Worker.
self.workers.local_worker().foreach_policy_to_train(update)
@override(Impala)
def _get_additional_update_kwargs(self, train_results) -> dict:
return dict(
last_update=self._counters[LAST_TARGET_UPDATE_TS],
mean_kl_loss_per_module={
module_id: r[LEARNER_RESULTS_KL_KEY]
for module_id, r in train_results.items()
if module_id != ALL_MODULES
},
)
@override(Impala)
def training_step(self) -> ResultDict:
train_results = super().training_step()
# Update KL, target network periodically.
self.after_train_step(train_results)
return train_results
@classmethod
@override(Impala)
def get_default_config(cls) -> AlgorithmConfig:
return APPOConfig()
@classmethod
@override(Impala)
def get_default_policy_class(
cls, config: AlgorithmConfig
) -> Optional[Type[Policy]]:
if config["framework"] == "torch":
from ray.rllib.algorithms.appo.appo_torch_policy import APPOTorchPolicy
return APPOTorchPolicy
elif config["framework"] == "tf":
if config._enable_new_api_stack:
raise ValueError(
"RLlib's RLModule and Learner API is not supported for"
" tf1. Use "
"framework='tf2' instead."
)
from ray.rllib.algorithms.appo.appo_tf_policy import APPOTF1Policy
return APPOTF1Policy
else:
from ray.rllib.algorithms.appo.appo_tf_policy import APPOTF2Policy
return APPOTF2Policy