wehub-resource-sync
101 lines
3.7 KiB
Python
101 lines
3.7 KiB
Python
"""
|
|
Default TQC RLModule.
|
|
|
|
TQC uses distributional critics with quantile regression.
|
|
"""
|
|
|
|
from typing import List, Tuple
|
|
|
|
from ray.rllib.core.learner.utils import make_target_network
|
|
from ray.rllib.core.rl_module.apis import InferenceOnlyAPI, QNetAPI, TargetNetworkAPI
|
|
from ray.rllib.core.rl_module.rl_module import RLModule
|
|
from ray.rllib.utils.annotations import (
|
|
override,
|
|
)
|
|
from ray.rllib.utils.typing import NetworkType
|
|
from ray.util.annotations import DeveloperAPI
|
|
|
|
|
|
@DeveloperAPI
|
|
class DefaultTQCRLModule(RLModule, InferenceOnlyAPI, TargetNetworkAPI, QNetAPI):
|
|
"""RLModule for the TQC (Truncated Quantile Critics) algorithm.
|
|
|
|
TQC extends SAC by using distributional critics with quantile regression.
|
|
Each critic outputs n_quantiles values instead of a single Q-value.
|
|
|
|
Architecture:
|
|
- Policy (Actor): Same as SAC
|
|
[obs] -> [pi_encoder] -> [pi_head] -> [action_dist_inputs]
|
|
|
|
- Quantile Critics: Multiple critics, each outputting n_quantiles
|
|
[obs, action] -> [qf_encoder_i] -> [qf_head_i] -> [n_quantiles values]
|
|
|
|
- Target Quantile Critics: Target networks for each critic
|
|
[obs, action] -> [target_qf_encoder_i] -> [target_qf_head_i] -> [n_quantiles]
|
|
"""
|
|
|
|
@override(RLModule)
|
|
def setup(self):
|
|
# TQC-specific parameters from model_config
|
|
self.n_quantiles = self.model_config.get("n_quantiles", 25)
|
|
self.n_critics = self.model_config.get("n_critics", 2)
|
|
self.top_quantiles_to_drop_per_net = self.model_config.get(
|
|
"top_quantiles_to_drop_per_net", 2
|
|
)
|
|
|
|
# Total quantiles across all critics
|
|
self.quantiles_total = self.n_quantiles * self.n_critics
|
|
|
|
# Build the encoder for the policy (same as SAC)
|
|
self.pi_encoder = self.catalog.build_encoder(framework=self.framework)
|
|
|
|
if not self.inference_only or self.framework != "torch":
|
|
# Build multiple Q-function encoders and heads
|
|
self.qf_encoders = []
|
|
self.qf_heads = []
|
|
|
|
for i in range(self.n_critics):
|
|
qf_encoder = self.catalog.build_qf_encoder(framework=self.framework)
|
|
qf_head = self.catalog.build_qf_head(framework=self.framework)
|
|
self.qf_encoders.append(qf_encoder)
|
|
self.qf_heads.append(qf_head)
|
|
|
|
# Build the policy head (same as SAC)
|
|
self.pi = self.catalog.build_pi_head(framework=self.framework)
|
|
|
|
@override(TargetNetworkAPI)
|
|
def make_target_networks(self):
|
|
"""Creates target networks for all quantile critics."""
|
|
self.target_qf_encoders = []
|
|
self.target_qf_heads = []
|
|
|
|
for i in range(self.n_critics):
|
|
target_encoder = make_target_network(self.qf_encoders[i])
|
|
target_head = make_target_network(self.qf_heads[i])
|
|
self.target_qf_encoders.append(target_encoder)
|
|
self.target_qf_heads.append(target_head)
|
|
|
|
@override(InferenceOnlyAPI)
|
|
def get_non_inference_attributes(self) -> List[str]:
|
|
"""Returns attributes not needed for inference."""
|
|
return [
|
|
"qf_encoders",
|
|
"qf_heads",
|
|
"target_qf_encoders",
|
|
"target_qf_heads",
|
|
]
|
|
|
|
@override(TargetNetworkAPI)
|
|
def get_target_network_pairs(self) -> List[Tuple[NetworkType, NetworkType]]:
|
|
"""Returns pairs of (network, target_network) for updating targets."""
|
|
pairs = []
|
|
for i in range(self.n_critics):
|
|
pairs.append((self.qf_encoders[i], self.target_qf_encoders[i]))
|
|
pairs.append((self.qf_heads[i], self.target_qf_heads[i]))
|
|
return pairs
|
|
|
|
@override(RLModule)
|
|
def get_initial_state(self) -> dict:
|
|
"""TQC does not support RNNs yet."""
|
|
return {}
|