Skip to content

Remove unnecessary feed_dicts for GAIL and Curiosity #2348

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
Jul 29, 2019
Merged

Remove unnecessary feed_dicts for GAIL and Curiosity #2348

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
7390f06
Remove some unneccessary feed dicts
Jul 26, 2019
59b26bd
Remove use_recurrent feed_dicts
Jul 27, 2019
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
20 changes: 1 addition & 19 deletions ml-agents/mlagents/trainers/components/reward_signals/curiosity/signal.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -70,12 +70,6 @@ def evaluate(
feed_dict[self.model.next_visual_in[i]] = next_info.visual_observations[i]
if self.policy.use_vec_obs:
feed_dict[self.model.next_vector_in] = next_info.vector_observations
if self.policy.use_recurrent:
if current_info.memories.shape[1] == 0:
current_info.memories = self.policy.make_empty_memory(
len(current_info.agents)
)
feed_dict[self.policy.model.memory_in] = current_info.memories
unscaled_reward = self.policy.sess.run(
self.model.intrinsic_reward, feed_dict=feed_dict
)
Expand Down Expand Up @@ -145,20 +139,10 @@ def _update_batch(
feed_dict[self.policy.model.output_pre] = mini_batch["actions_pre"].reshape(
[-1, self.policy.model.act_size[0]]
)
feed_dict[self.policy.model.epsilon] = mini_batch[
"random_normal_epsilon"
].reshape([-1, self.policy.model.act_size[0]])
else:
feed_dict[self.policy.model.action_holder] = mini_batch["actions"].reshape(
[-1, len(self.policy.model.act_size)]
)
if self.policy.use_recurrent:
feed_dict[self.policy.model.prev_action] = mini_batch[
"prev_action"
].reshape([-1, len(self.policy.model.act_size)])
feed_dict[self.policy.model.action_masks] = mini_batch[
"action_mask"
].reshape([-1, sum(self.policy.brain.vector_action_space_size)])
if self.policy.use_vec_obs:
feed_dict[self.policy.model.vector_in] = mini_batch["vector_obs"].reshape(
[-1, self.policy.vec_obs_size]
Expand All @@ -185,9 +169,7 @@ def _update_batch(
)
else:
feed_dict[self.model.next_visual_in[i]] = _obs
if self.policy.use_recurrent:
mem_in = mini_batch["memory"][:, 0, :]
feed_dict[self.policy.model.memory_in] = mem_in

self.has_updated = True
run_out = self.policy._execute_model(feed_dict, self.update_dict)
return run_out
6 changes: 0 additions & 6 deletions ml-agents/mlagents/trainers/components/reward_signals/gail/signal.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -75,12 +75,6 @@ def evaluate(
feed_dict[
self.policy.model.action_holder
] = next_info.previous_vector_actions
if self.policy.use_recurrent:
if current_info.memories.shape[1] == 0:
current_info.memories = self.policy.make_empty_memory(
len(current_info.agents)
)
feed_dict[self.policy.model.memory_in] = current_info.memories
unscaled_reward = self.policy.sess.run(
self.model.intrinsic_reward, feed_dict=feed_dict
)
Expand Down