Skip to content

Fix for Discrete observations + Curiosity #866

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Jun 15, 2018
Merged

Fix for Discrete observations + Curiosity #866

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
3dafe5c
Fix for Discrete observations + Curiosity
awjuliani Jun 15, 2018
3cd7ceb
Merge branch 'release-v0.4' into release-fix-discrete-observations
awjuliani Jun 15, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion python/unitytrainers/models.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -150,7 +150,7 @@ def create_discrete_observation_encoder(observation_input, s_size, h_size, activ
:param num_layers: number of hidden layers to create.
:return: List of hidden layer tensors.
"""
with tf.name_scope(scope):
with tf.variable_scope(scope):
vector_in = tf.reshape(observation_input, [-1])
state_onehot = tf.one_hot(vector_in, s_size)
hidden = state_onehot
Expand Down
36 changes: 26 additions & 10 deletions python/unitytrainers/ppo/models.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -91,18 +91,34 @@ def create_curiosity_encoders(self):
encoded_next_state_list.append(hidden_next_visual)

if self.o_size > 0:
# Create input op for next (t+1) vector observation.
self.next_vector_in = tf.placeholder(shape=[None, self.o_size], dtype=tf.float32,
name='next_vector_observation')

# Create the encoder ops for current and next vector input. Not that these encoders are siamese.
encoded_vector_obs = self.create_continuous_observation_encoder(self.vector_in,
self.curiosity_enc_size,
self.swish, 2, "vector_obs_encoder", False)
encoded_next_vector_obs = self.create_continuous_observation_encoder(self.next_vector_in,
self.curiosity_enc_size,
self.swish, 2, "vector_obs_encoder",
True)
if self.brain.vector_observation_space_type == "continuous":
# Create input op for next (t+1) vector observation.
self.next_vector_in = tf.placeholder(shape=[None, self.o_size], dtype=tf.float32,
name='next_vector_observation')

encoded_vector_obs = self.create_continuous_observation_encoder(self.vector_in,
self.curiosity_enc_size,
self.swish, 2, "vector_obs_encoder",
False)
encoded_next_vector_obs = self.create_continuous_observation_encoder(self.next_vector_in,
self.curiosity_enc_size,
self.swish, 2,
"vector_obs_encoder",
True)
else:
self.next_vector_in = tf.placeholder(shape=[None, 1], dtype=tf.int32,
name='next_vector_observation')

encoded_vector_obs = self.create_discrete_observation_encoder(self.vector_in, self.o_size,
self.curiosity_enc_size,
self.swish, 2, "vector_obs_encoder",
False)
encoded_next_vector_obs = self.create_discrete_observation_encoder(self.next_vector_in, self.o_size,
self.curiosity_enc_size,
self.swish, 2, "vector_obs_encoder",
True)
encoded_state_list.append(encoded_vector_obs)
encoded_next_state_list.append(encoded_next_vector_obs)

Expand Down
3 changes: 3 additions & 0 deletions python/unitytrainers/ppo/trainer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -432,6 +432,9 @@ def update_model(self):
else:
feed_dict[self.model.vector_in] = np.array(buffer['vector_obs'][start:end]).reshape(
[-1, self.brain.num_stacked_vector_observations])
if self.use_curiosity:
feed_dict[self.model.next_vector_in] = np.array(buffer['next_vector_in'][start:end]) \
.reshape([-1, self.brain.num_stacked_vector_observations])
if self.use_visual_obs:
for i, _ in enumerate(self.model.visual_in):
_obs = np.array(buffer['visual_obs%d' % i][start:end])
Expand Down