"""Asynchronous Proximal Policy Optimization (APPO)
The algorithm is described in [1] (under the name of "IMPACT"):
Detailed documentation:
https://docs.ray.io/en/master/rllib-algorithms.html#appo
[1] IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks.
Luo et al. 2020
https://arxiv.org/pdf/1912.00167
"""
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 RLModuleSpec
from ray.rllib.policy.policy import Policy
from ray.rllib.utils.annotations import override
from ray.rllib.utils.deprecation import DEPRECATED_VALUE, deprecation_warning
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 LEARNER_STATS_KEY
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"
[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_per_learner=50)
)
config = config.learners(num_learners=1)
config = config.env_runners(num_env_runners=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 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=tune.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."""
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).
}
super().__init__(algo_class=algo_class or APPO)
# fmt: off
# __sphinx_doc_begin__
# APPO specific settings:
self.vtrace = 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
self.target_worker_clipping = 2.0
# Circular replay buffer settings.
# Used in [1] for discrete action tasks:
# `circular_buffer_num_batches=4` and `circular_buffer_iterations_per_batch=2`
# For cont. action tasks:
# `circular_buffer_num_batches=16` and `circular_buffer_iterations_per_batch=20`
self.circular_buffer_num_batches = 4
self.circular_buffer_iterations_per_batch = 2
# Override some of IMPALAConfig's default values with APPO-specific values.
self.num_env_runners = 2
self.target_network_update_freq = 2
self.broadcast_interval = 1
self.grad_clip = 40.0
# Note: Only when using enable_rl_module_and_learner=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.decay = 0.99
self.momentum = 0.0
self.epsilon = 0.1
self.vf_loss_coeff = 0.5
self.entropy_coeff = 0.01
self.tau = 1.0
# __sphinx_doc_end__
# fmt: on
self.lr_schedule = None # @OldAPIStack
self.entropy_coeff_schedule = None # @OldAPIStack
self.num_gpus = 0 # @OldAPIStack
self.num_multi_gpu_tower_stacks = 1 # @OldAPIStack
self.minibatch_buffer_size = 1 # @OldAPIStack
self.replay_proportion = 0.0 # @OldAPIStack
self.replay_buffer_num_slots = 100 # @OldAPIStack
self.learner_queue_size = 16 # @OldAPIStack
self.learner_queue_timeout = 300 # @OldAPIStack
# Deprecated keys.
self.target_update_frequency = DEPRECATED_VALUE
self.use_critic = DEPRECATED_VALUE
[docs]
@override(IMPALAConfig)
def training(
self,
*,
vtrace: 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,
target_network_update_freq: Optional[int] = NotProvided,
tau: Optional[float] = NotProvided,
target_worker_clipping: Optional[float] = NotProvided,
circular_buffer_num_batches: Optional[int] = NotProvided,
circular_buffer_iterations_per_batch: Optional[int] = NotProvided,
# Deprecated keys.
target_update_frequency=DEPRECATED_VALUE,
use_critic=DEPRECATED_VALUE,
**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_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).
target_network_update_freq: NOTE: This parameter is only applicable on
the new API stack. The frequency with which to update the target
policy network from the main trained policy network. The metric
used is `NUM_ENV_STEPS_TRAINED_LIFETIME` and the unit is `n` (see [1]
4.1.1), where: `n = [circular_buffer_num_batches (N)] *
[circular_buffer_iterations_per_batch (K)] * [train batch size]`
For example, if you set `target_network_update_freq=2`, and N=4, K=2,
and `train_batch_size_per_learner=500`, then the target net is updated
every 2*4*2*500=8000 trained env steps (every 16 batch updates on each
learner).
The authors in [1] suggests that this setting is robust to a range of
choices (try values between 0.125 and 4).
target_network_update_freq: 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_network_update_freq` number of environment samples to be trained
on before updating the target networks and tune the kl loss
coefficients. NOTE: This parameter is only applicable when using the
Learner API (enable_rl_module_and_learner=True).
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_worker_clipping: The maximum value for the target-worker-clipping
used for computing the IS ratio, described in [1]
IS = min(π(i) / π(target), ρ) * (π / π(i))
circular_buffer_num_batches: The number of train batches that fit
into the circular buffer. Each such train batch can be sampled for
training max. `circular_buffer_iterations_per_batch` times.
circular_buffer_iterations_per_batch: The number of times any train
batch in the circular buffer can be sampled for training. A batch gets
evicted from the buffer either if it's the oldest batch in the buffer
and a new batch is added OR if the batch reaches this max. number of
being sampled.
Returns:
This updated AlgorithmConfig object.
"""
if target_update_frequency != DEPRECATED_VALUE:
deprecation_warning(
old="target_update_frequency",
new="target_network_update_freq",
error=True,
)
if use_critic != DEPRECATED_VALUE:
deprecation_warning(
old="use_critic",
help="`use_critic` no longer supported! APPO always uses a value "
"function (critic).",
error=True,
)
# Pass kwargs onto super's `training()` method.
super().training(**kwargs)
if vtrace is not NotProvided:
self.vtrace = vtrace
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 target_network_update_freq is not NotProvided:
self.target_network_update_freq = target_network_update_freq
if tau is not NotProvided:
self.tau = tau
if target_worker_clipping is not NotProvided:
self.target_worker_clipping = target_worker_clipping
if circular_buffer_num_batches is not NotProvided:
self.circular_buffer_num_batches = circular_buffer_num_batches
if circular_buffer_iterations_per_batch is not NotProvided:
self.circular_buffer_iterations_per_batch = (
circular_buffer_iterations_per_batch
)
return self
@override(IMPALAConfig)
def validate(self) -> None:
super().validate()
# On new API stack, circular buffer should be used, not `minibatch_buffer_size`.
if self.enable_rl_module_and_learner:
if self.minibatch_buffer_size != 1 or self.replay_proportion != 0.0:
self._value_error(
"`minibatch_buffer_size/replay_proportion` not valid on new API "
"stack with APPO! "
"Use `circular_buffer_num_batches` for the number of train batches "
"in the circular buffer. To change the maximum number of times "
"any batch may be sampled, set "
"`circular_buffer_iterations_per_batch`."
)
if self.num_multi_gpu_tower_stacks != 1:
self._value_error(
"`num_multi_gpu_tower_stacks` not supported on new API stack with "
"APPO! In order to train on multi-GPU, use "
"`config.learners(num_learners=[number of GPUs], "
"num_gpus_per_learner=1)`. To scale the throughput of batch-to-GPU-"
"pre-loading on each of your `Learners`, set "
"`num_gpu_loader_threads` to a higher number (recommended values: "
"1-8)."
)
if self.learner_queue_size != 16:
self._value_error(
"`learner_queue_size` not supported on new API stack with "
"APPO! In order set the size of the circular buffer (which acts as "
"a 'learner queue'), use "
"`config.training(circular_buffer_num_batches=..)`. To change the "
"maximum number of times any batch may be sampled, set "
"`config.training(circular_buffer_iterations_per_batch=..)`."
)
@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 in ["tf2", "tf"]:
raise ValueError(
"TensorFlow is no longer supported on the new API stack! "
"Use `framework='torch'`."
)
else:
raise ValueError(
f"The framework {self.framework_str} is not supported. "
"Use `framework='torch'`."
)
@override(IMPALAConfig)
def get_default_rl_module_spec(self) -> RLModuleSpec:
if self.framework_str == "torch":
from ray.rllib.algorithms.appo.torch.appo_torch_rl_module import (
APPOTorchRLModule as RLModule,
)
else:
raise ValueError(
f"The framework {self.framework_str} is not supported. "
"Use either 'torch' or 'tf2'."
)
return RLModuleSpec(module_class=RLModule)
@property
@override(AlgorithmConfig)
def _model_config_auto_includes(self):
return super()._model_config_auto_includes | {"vf_share_layers": False}
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_rl_module_and_learner:
self.env_runner.foreach_policy_to_train(lambda p, _: p.update_target())
@override(IMPALA)
def training_step(self) -> None:
if self.config.enable_rl_module_and_learner:
return super().training_step()
train_results = super().training_step()
# Update the target network and the KL coefficient for the APPO-loss.
# The target network update frequency is calculated automatically by the product
# of `num_epochs` setting (usually 1 for APPO) and `minibatch_buffer_size`.
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_epochs * 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.env_runner.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.env_runner.foreach_policy_to_train(update)
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_rl_module_and_learner:
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