mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
99a0088233
* [RLlib] Unify the way we create and use LocalReplayBuffer for all the agents. This change 1. Get rid of the try...except clause when we call execution_plan(), and get rid of the Deprecation warning as a result. 2. Fix the execution_plan() call in Trainer._try_recover() too. 3. Most importantly, makes it much easier to create and use different types of local replay buffers for all our agents. E.g., allow us to easily create a reservoir sampling replay buffer for APPO agent for Riot in the near future. * Introduce explicit configuration for replay buffer types. * Fix is_training key error. * actually deprecate buffer_size field.
295 lines
12 KiB
Python
295 lines
12 KiB
Python
import gym
|
|
from gym.spaces import Box, Discrete
|
|
import numpy as np
|
|
import tree # pip install dm_tree
|
|
from typing import Dict, List, Optional
|
|
|
|
from ray.rllib.models.catalog import ModelCatalog
|
|
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
|
|
from ray.rllib.utils import force_list
|
|
from ray.rllib.utils.annotations import override
|
|
from ray.rllib.utils.framework import try_import_tf
|
|
from ray.rllib.utils.spaces.simplex import Simplex
|
|
from ray.rllib.utils.typing import ModelConfigDict, TensorType
|
|
|
|
tf1, tf, tfv = try_import_tf()
|
|
|
|
|
|
class SACTFModel(TFModelV2):
|
|
"""Extension of the standard TFModelV2 for SAC.
|
|
|
|
To customize, do one of the following:
|
|
- sub-class SACTFModel and override one or more of its methods.
|
|
- Use SAC's `Q_model` and `policy_model` keys to tweak the default model
|
|
behaviors (e.g. fcnet_hiddens, conv_filters, etc..).
|
|
- Use SAC's `Q_model->custom_model` and `policy_model->custom_model` keys
|
|
to specify your own custom Q-model(s) and policy-models, which will be
|
|
created within this SACTFModel (see `build_policy_model` and
|
|
`build_q_model`.
|
|
|
|
Note: It is not recommended to override the `forward` method for SAC. This
|
|
would lead to shared weights (between policy and Q-nets), which will then
|
|
not be optimized by either of the critic- or actor-optimizers!
|
|
|
|
Data flow:
|
|
`obs` -> forward() (should stay a noop method!) -> `model_out`
|
|
`model_out` -> get_policy_output() -> pi(actions|obs)
|
|
`model_out`, `actions` -> get_q_values() -> Q(s, a)
|
|
`model_out`, `actions` -> get_twin_q_values() -> Q_twin(s, a)
|
|
"""
|
|
|
|
def __init__(self,
|
|
obs_space: gym.spaces.Space,
|
|
action_space: gym.spaces.Space,
|
|
num_outputs: Optional[int],
|
|
model_config: ModelConfigDict,
|
|
name: str,
|
|
policy_model_config: ModelConfigDict = None,
|
|
q_model_config: ModelConfigDict = None,
|
|
twin_q: bool = False,
|
|
initial_alpha: float = 1.0,
|
|
target_entropy: Optional[float] = None):
|
|
"""Initialize a SACTFModel instance.
|
|
|
|
Args:
|
|
policy_model_config (ModelConfigDict): The config dict for the
|
|
policy network.
|
|
q_model_config (ModelConfigDict): The config dict for the
|
|
Q-network(s) (2 if twin_q=True).
|
|
twin_q (bool): Build twin Q networks (Q-net and target) for more
|
|
stable Q-learning.
|
|
initial_alpha (float): The initial value for the to-be-optimized
|
|
alpha parameter (default: 1.0).
|
|
target_entropy (Optional[float]): A target entropy value for
|
|
the to-be-optimized alpha parameter. If None, will use the
|
|
defaults described in the papers for SAC (and discrete SAC).
|
|
|
|
Note that the core layers for forward() are not defined here, this
|
|
only defines the layers for the output heads. Those layers for
|
|
forward() should be defined in subclasses of SACModel.
|
|
"""
|
|
super(SACTFModel, self).__init__(obs_space, action_space, num_outputs,
|
|
model_config, name)
|
|
if isinstance(action_space, Discrete):
|
|
self.action_dim = action_space.n
|
|
self.discrete = True
|
|
action_outs = q_outs = self.action_dim
|
|
elif isinstance(action_space, Box):
|
|
self.action_dim = np.product(action_space.shape)
|
|
self.discrete = False
|
|
action_outs = 2 * self.action_dim
|
|
q_outs = 1
|
|
else:
|
|
assert isinstance(action_space, Simplex)
|
|
self.action_dim = np.product(action_space.shape)
|
|
self.discrete = False
|
|
action_outs = self.action_dim
|
|
q_outs = 1
|
|
|
|
self.action_model = self.build_policy_model(
|
|
self.obs_space, action_outs, policy_model_config, "policy_model")
|
|
|
|
self.q_net = self.build_q_model(self.obs_space, self.action_space,
|
|
q_outs, q_model_config, "q")
|
|
if twin_q:
|
|
self.twin_q_net = self.build_q_model(self.obs_space,
|
|
self.action_space, q_outs,
|
|
q_model_config, "twin_q")
|
|
else:
|
|
self.twin_q_net = None
|
|
|
|
self.log_alpha = tf.Variable(
|
|
np.log(initial_alpha), dtype=tf.float32, name="log_alpha")
|
|
self.alpha = tf.exp(self.log_alpha)
|
|
|
|
# Auto-calculate the target entropy.
|
|
if target_entropy is None or target_entropy == "auto":
|
|
# See hyperparams in [2] (README.md).
|
|
if self.discrete:
|
|
target_entropy = 0.98 * np.array(
|
|
-np.log(1.0 / action_space.n), dtype=np.float32)
|
|
# See [1] (README.md).
|
|
else:
|
|
target_entropy = -np.prod(action_space.shape)
|
|
self.target_entropy = target_entropy
|
|
|
|
@override(TFModelV2)
|
|
def forward(self, input_dict: Dict[str, TensorType],
|
|
state: List[TensorType],
|
|
seq_lens: TensorType) -> (TensorType, List[TensorType]):
|
|
"""The common (Q-net and policy-net) forward pass.
|
|
|
|
NOTE: It is not(!) recommended to override this method as it would
|
|
introduce a shared pre-network, which would be updated by both
|
|
actor- and critic optimizers.
|
|
"""
|
|
return input_dict["obs"], state
|
|
|
|
def build_policy_model(self, obs_space, num_outputs, policy_model_config,
|
|
name):
|
|
"""Builds the policy model used by this SAC.
|
|
|
|
Override this method in a sub-class of SACTFModel to implement your
|
|
own policy net. Alternatively, simply set `custom_model` within the
|
|
top level SAC `policy_model` config key to make this default
|
|
implementation of `build_policy_model` use your custom policy network.
|
|
|
|
Returns:
|
|
TFModelV2: The TFModelV2 policy sub-model.
|
|
"""
|
|
model = ModelCatalog.get_model_v2(
|
|
obs_space,
|
|
self.action_space,
|
|
num_outputs,
|
|
policy_model_config,
|
|
framework="tf",
|
|
name=name)
|
|
return model
|
|
|
|
def build_q_model(self, obs_space, action_space, num_outputs,
|
|
q_model_config, name):
|
|
"""Builds one of the (twin) Q-nets used by this SAC.
|
|
|
|
Override this method in a sub-class of SACTFModel to implement your
|
|
own Q-nets. Alternatively, simply set `custom_model` within the
|
|
top level SAC `Q_model` config key to make this default implementation
|
|
of `build_q_model` use your custom Q-nets.
|
|
|
|
Returns:
|
|
TFModelV2: The TFModelV2 Q-net sub-model.
|
|
"""
|
|
self.concat_obs_and_actions = False
|
|
if self.discrete:
|
|
input_space = obs_space
|
|
else:
|
|
orig_space = getattr(obs_space, "original_space", obs_space)
|
|
if isinstance(orig_space, Box) and len(orig_space.shape) == 1:
|
|
input_space = Box(
|
|
float("-inf"),
|
|
float("inf"),
|
|
shape=(orig_space.shape[0] + action_space.shape[0], ))
|
|
self.concat_obs_and_actions = True
|
|
else:
|
|
if isinstance(orig_space, gym.spaces.Tuple):
|
|
spaces = list(orig_space.spaces)
|
|
elif isinstance(orig_space, gym.spaces.Dict):
|
|
spaces = list(orig_space.spaces.values())
|
|
else:
|
|
spaces = [obs_space]
|
|
input_space = gym.spaces.Tuple(spaces + [action_space])
|
|
|
|
model = ModelCatalog.get_model_v2(
|
|
input_space,
|
|
action_space,
|
|
num_outputs,
|
|
q_model_config,
|
|
framework="tf",
|
|
name=name)
|
|
return model
|
|
|
|
def get_q_values(self,
|
|
model_out: TensorType,
|
|
actions: Optional[TensorType] = None) -> TensorType:
|
|
"""Returns Q-values, given the output of self.__call__().
|
|
|
|
This implements Q(s, a) -> [single Q-value] for the continuous case and
|
|
Q(s) -> [Q-values for all actions] for the discrete case.
|
|
|
|
Args:
|
|
model_out (TensorType): Feature outputs from the model layers
|
|
(result of doing `self.__call__(obs)`).
|
|
actions (Optional[TensorType]): Continuous action batch to return
|
|
Q-values for. Shape: [BATCH_SIZE, action_dim]. If None
|
|
(discrete action case), return Q-values for all actions.
|
|
|
|
Returns:
|
|
TensorType: Q-values tensor of shape [BATCH_SIZE, 1].
|
|
"""
|
|
return self._get_q_value(model_out, actions, self.q_net)
|
|
|
|
def get_twin_q_values(self,
|
|
model_out: TensorType,
|
|
actions: Optional[TensorType] = None) -> TensorType:
|
|
"""Same as get_q_values but using the twin Q net.
|
|
|
|
This implements the twin Q(s, a).
|
|
|
|
Args:
|
|
model_out (TensorType): Feature outputs from the model layers
|
|
(result of doing `self.__call__(obs)`).
|
|
actions (Optional[Tensor]): Actions to return the Q-values for.
|
|
Shape: [BATCH_SIZE, action_dim]. If None (discrete action
|
|
case), return Q-values for all actions.
|
|
|
|
Returns:
|
|
TensorType: Q-values tensor of shape [BATCH_SIZE, 1].
|
|
"""
|
|
return self._get_q_value(model_out, actions, self.twin_q_net)
|
|
|
|
def _get_q_value(self, model_out, actions, net):
|
|
# Model outs may come as original Tuple/Dict observations, concat them
|
|
# here if this is the case.
|
|
if isinstance(net.obs_space, Box):
|
|
if isinstance(model_out, (list, tuple)):
|
|
model_out = tf.concat(model_out, axis=-1)
|
|
elif isinstance(model_out, dict):
|
|
model_out = tf.concat(list(model_out.values()), axis=-1)
|
|
elif isinstance(model_out, dict):
|
|
model_out = list(model_out.values())
|
|
|
|
# Continuous case -> concat actions to model_out.
|
|
if actions is not None:
|
|
if self.concat_obs_and_actions:
|
|
input_dict = {"obs": tf.concat([model_out, actions], axis=-1)}
|
|
else:
|
|
input_dict = {"obs": force_list(model_out) + [actions]}
|
|
# Discrete case -> return q-vals for all actions.
|
|
else:
|
|
input_dict = {"obs": model_out}
|
|
# Switch on training mode (when getting Q-values, we are usually in
|
|
# training).
|
|
input_dict["is_training"] = True
|
|
|
|
out, _ = net(input_dict, [], None)
|
|
return out
|
|
|
|
def get_policy_output(self, model_out: TensorType) -> TensorType:
|
|
"""Returns policy outputs, given the output of self.__call__().
|
|
|
|
For continuous action spaces, these will be the mean/stddev
|
|
distribution inputs for the (SquashedGaussian) action distribution.
|
|
For discrete action spaces, these will be the logits for a categorical
|
|
distribution.
|
|
|
|
Args:
|
|
model_out (TensorType): Feature outputs from the model layers
|
|
(result of doing `self.__call__(obs)`).
|
|
|
|
Returns:
|
|
TensorType: Distribution inputs for sampling actions.
|
|
"""
|
|
# Model outs may come as original Tuple/Dict observations, concat them
|
|
# here if this is the case.
|
|
if isinstance(self.action_model.obs_space, Box):
|
|
if isinstance(model_out, (list, tuple)):
|
|
model_out = tf.concat(model_out, axis=-1)
|
|
elif isinstance(model_out, dict):
|
|
model_out = tf.concat(
|
|
[
|
|
tf.expand_dims(val, 1) if len(val.shape) == 1 else val
|
|
for val in tree.flatten(model_out.values())
|
|
],
|
|
axis=-1)
|
|
out, _ = self.action_model({"obs": model_out}, [], None)
|
|
return out
|
|
|
|
def policy_variables(self):
|
|
"""Return the list of variables for the policy net."""
|
|
|
|
return self.action_model.variables()
|
|
|
|
def q_variables(self):
|
|
"""Return the list of variables for Q / twin Q nets."""
|
|
|
|
return self.q_net.variables() + (self.twin_q_net.variables()
|
|
if self.twin_q_net else [])
|