mirror of
https://github.com/vale981/ray
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220 lines
9 KiB
Python
220 lines
9 KiB
Python
import gym
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from gym.spaces import Discrete
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import numpy as np
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from typing import Optional, Tuple
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from ray.rllib.models.tf.tf_modelv2 import TFModelV2
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from ray.rllib.utils.framework import try_import_tf
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from ray.rllib.utils.typing import ModelConfigDict, TensorType
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tf1, tf, tfv = try_import_tf()
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class SACTFModel(TFModelV2):
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"""Extension of the standard TFModelV2 for SAC.
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Instances of this Model get created via wrapping this class around another
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default- or custom model (inside
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rllib/agents/sac/sac_tf_policy.py::build_sac_model). Doing so simply adds
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this class' methods (`get_q_values`, etc..) to the wrapped model, such that
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the wrapped model can be used by the SAC algorithm.
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Data flow:
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`obs` -> forward() -> `model_out`
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`model_out` -> get_policy_output() -> pi(actions|obs)
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`model_out`, `actions` -> get_q_values() -> Q(s, a)
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`model_out`, `actions` -> get_twin_q_values() -> Q_twin(s, a)
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"""
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def __init__(self,
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obs_space: gym.spaces.Space,
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action_space: gym.spaces.Space,
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num_outputs: Optional[int],
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model_config: ModelConfigDict,
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name: str,
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actor_hidden_activation: str = "relu",
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actor_hiddens: Tuple[int] = (256, 256),
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critic_hidden_activation: str = "relu",
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critic_hiddens: Tuple[int] = (256, 256),
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twin_q: bool = False,
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initial_alpha: float = 1.0,
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target_entropy: Optional[float] = None):
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"""Initialize a SACTFModel instance.
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Args:
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actor_hidden_activation (str): Activation for the actor network.
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actor_hiddens (list): Hidden layers sizes for the actor network.
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critic_hidden_activation (str): Activation for the critic network.
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critic_hiddens (list): Hidden layers sizes for the critic network.
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twin_q (bool): Build twin Q networks (Q-net and target) for more
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stable Q-learning.
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initial_alpha (float): The initial value for the to-be-optimized
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alpha parameter (default: 1.0).
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target_entropy (Optional[float]): A target entropy value for
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the to-be-optimized alpha parameter. If None, will use the
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defaults described in the papers for SAC (and discrete SAC).
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Note that the core layers for forward() are not defined here, this
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only defines the layers for the output heads. Those layers for
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forward() should be defined in subclasses of SACModel.
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"""
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super(SACTFModel, self).__init__(obs_space, action_space, num_outputs,
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model_config, name)
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if isinstance(action_space, Discrete):
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self.action_dim = action_space.n
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self.discrete = True
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action_outs = q_outs = self.action_dim
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else:
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self.action_dim = np.product(action_space.shape)
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self.discrete = False
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action_outs = 2 * self.action_dim
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q_outs = 1
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self.model_out = tf.keras.layers.Input(
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shape=(self.num_outputs, ), name="model_out")
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self.action_model = tf.keras.Sequential([
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tf.keras.layers.Dense(
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units=hidden,
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activation=getattr(tf.nn, actor_hidden_activation, None),
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name="action_{}".format(i + 1))
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for i, hidden in enumerate(actor_hiddens)
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] + [
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tf.keras.layers.Dense(
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units=action_outs, activation=None, name="action_out")
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])
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self.shift_and_log_scale_diag = self.action_model(self.model_out)
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self.register_variables(self.action_model.variables)
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self.actions_input = None
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if not self.discrete:
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self.actions_input = tf.keras.layers.Input(
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shape=(self.action_dim, ), name="actions")
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def build_q_net(name, observations, actions):
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# For continuous actions: Feed obs and actions (concatenated)
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# through the NN. For discrete actions, only obs.
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q_net = tf.keras.Sequential(([
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tf.keras.layers.Concatenate(axis=1),
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] if not self.discrete else []) + [
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tf.keras.layers.Dense(
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units=units,
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activation=getattr(tf.nn, critic_hidden_activation, None),
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name="{}_hidden_{}".format(name, i))
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for i, units in enumerate(critic_hiddens)
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] + [
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tf.keras.layers.Dense(
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units=q_outs, activation=None, name="{}_out".format(name))
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])
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# TODO(hartikainen): Remove the unnecessary Model calls here
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if self.discrete:
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q_net = tf.keras.Model(observations, q_net(observations))
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else:
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q_net = tf.keras.Model([observations, actions],
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q_net([observations, actions]))
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return q_net
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self.q_net = build_q_net("q", self.model_out, self.actions_input)
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self.register_variables(self.q_net.variables)
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if twin_q:
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self.twin_q_net = build_q_net("twin_q", self.model_out,
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self.actions_input)
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self.register_variables(self.twin_q_net.variables)
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else:
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self.twin_q_net = None
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self.log_alpha = tf.Variable(
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np.log(initial_alpha), dtype=tf.float32, name="log_alpha")
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self.alpha = tf.exp(self.log_alpha)
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# Auto-calculate the target entropy.
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if target_entropy is None or target_entropy == "auto":
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# See hyperparams in [2] (README.md).
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if self.discrete:
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target_entropy = 0.98 * np.array(
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-np.log(1.0 / action_space.n), dtype=np.float32)
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# See [1] (README.md).
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else:
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target_entropy = -np.prod(action_space.shape)
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self.target_entropy = target_entropy
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self.register_variables([self.log_alpha])
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def get_q_values(self,
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model_out: TensorType,
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actions: Optional[TensorType] = None) -> TensorType:
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"""Returns Q-values, given the output of self.__call__().
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This implements Q(s, a) -> [single Q-value] for the continuous case and
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Q(s) -> [Q-values for all actions] for the discrete case.
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Args:
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model_out (TensorType): Feature outputs from the model layers
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(result of doing `self.__call__(obs)`).
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actions (Optional[TensorType]): Continuous action batch to return
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Q-values for. Shape: [BATCH_SIZE, action_dim]. If None
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(discrete action case), return Q-values for all actions.
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Returns:
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TensorType: Q-values tensor of shape [BATCH_SIZE, 1].
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"""
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# Continuous case -> concat actions to model_out.
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if actions is not None:
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return self.q_net([model_out, actions])
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# Discrete case -> return q-vals for all actions.
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else:
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return self.q_net(model_out)
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def get_twin_q_values(self,
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model_out: TensorType,
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actions: Optional[TensorType] = None) -> TensorType:
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"""Same as get_q_values but using the twin Q net.
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This implements the twin Q(s, a).
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Args:
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model_out (TensorType): Feature outputs from the model layers
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(result of doing `self.__call__(obs)`).
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actions (Optional[Tensor]): Actions to return the Q-values for.
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Shape: [BATCH_SIZE, action_dim]. If None (discrete action
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case), return Q-values for all actions.
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Returns:
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TensorType: Q-values tensor of shape [BATCH_SIZE, 1].
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"""
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# Continuous case -> concat actions to model_out.
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if actions is not None:
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return self.twin_q_net([model_out, actions])
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# Discrete case -> return q-vals for all actions.
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else:
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return self.twin_q_net(model_out)
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def get_policy_output(self, model_out: TensorType) -> TensorType:
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"""Returns policy outputs, given the output of self.__call__().
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For continuous action spaces, these will be the mean/stddev
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distribution inputs for the (SquashedGaussian) action distribution.
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For discrete action spaces, these will be the logits for a categorical
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distribution.
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Args:
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model_out (TensorType): Feature outputs from the model layers
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(result of doing `self.__call__(obs)`).
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Returns:
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TensorType: Distribution inputs for sampling actions.
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"""
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return self.action_model(model_out)
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def policy_variables(self):
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"""Return the list of variables for the policy net."""
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return list(self.action_model.variables)
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def q_variables(self):
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"""Return the list of variables for Q / twin Q nets."""
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return self.q_net.variables + (self.twin_q_net.variables
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if self.twin_q_net else [])
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