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Added Custom Initialisation for MXNet Heads (#86)

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* Added NormalizedRSSInitializer, using same method as TensorFlow backend, but changed name since ‘columns’ have different meaning in dense layer weight matrix in MXNet.

* Added unit test for NormalizedRSSInitializer.
This commit is contained in:
Thom Lane
2018-11-16 08:15:43 -08:00
committed by Scott Leishman
parent 101c55d37d
commit 81bac050d7
5 changed files with 59 additions and 8 deletions
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10
rl_coach/architectures/mxnet_components/heads/ppo_head.py
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@@ -8,7 +8,8 @@ from rl_coach.base_parameters import AgentParameters
from rl_coach.core_types import ActionProbabilities
from rl_coach.spaces import SpacesDefinition, BoxActionSpace, DiscreteActionSpace
from rl_coach.utils import eps
from rl_coach.architectures.mxnet_components.heads.head import Head, HeadLoss, LossInputSchema
from rl_coach.architectures.mxnet_components.heads.head import Head, HeadLoss, LossInputSchema,\
NormalizedRSSInitializer
from rl_coach.architectures.mxnet_components.heads.head import LOSS_OUT_TYPE_LOSS, LOSS_OUT_TYPE_REGULARIZATION
from rl_coach.architectures.mxnet_components.utils import hybrid_clip
@@ -229,7 +230,8 @@ class DiscretePPOHead(nn.HybridBlock):
"""
super(DiscretePPOHead, self).__init__()
with self.name_scope():
self.dense = nn.Dense(units=num_actions, flatten=False)
self.dense = nn.Dense(units=num_actions, flatten=False,
weight_initializer=NormalizedRSSInitializer(0.01))
def hybrid_forward(self, F: ModuleType, x: nd_sym_type) -> nd_sym_type:
"""
@@ -258,8 +260,8 @@ class ContinuousPPOHead(nn.HybridBlock):
"""
super(ContinuousPPOHead, self).__init__()
with self.name_scope():
# todo: change initialization strategy
self.dense = nn.Dense(units=num_actions, flatten=False)
self.dense = nn.Dense(units=num_actions, flatten=False,
weight_initializer=NormalizedRSSInitializer(0.01))
# all samples (across batch, and time step) share the same covariance, which is learnt,
# but since we assume the action probability variables are independent,
# only the diagonal entries of the covariance matrix are specified.