Skip to content
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

feature: data gathering code both for solo/multi player training #341

Merged
merged 6 commits into from
Apr 18, 2023
Merged

feature: data gathering code both for solo/multi player training #341

Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
533278a
Merge pull request #308 from DeNA/develop
ikki407 May 9, 2022
f66eedd
Merge pull request #319 from DeNA/develop
ikki407 Jun 27, 2022
8fb63a3
Merge pull request #325 from DeNA/develop
ikki407 Nov 2, 2022
3b375e1
feature: data gathering code both for solo/multi player training
YuriCat Jan 5, 2023
6bdcd87
Merge pull request #334 from DeNA/develop
YuriCat Feb 26, 2023
f701263
Merge branch 'develop' into feature/same_batch_generation_code_for_bo…
YuriCat Apr 17, 2023
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
15 changes: 7 additions & 8 deletions handyrl/train.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -63,15 +63,14 @@ def replace_none(a, b):

# data that is changed by training configuration
if args['turn_based_training'] and not args['observation']:
obs = [[m['observation'][m['turn'][0]]] for m in moments]
prob = np.array([[[m['selected_prob'][m['turn'][0]]]] for m in moments])
act = np.array([[m['action'][m['turn'][0]]] for m in moments], dtype=np.int64)[..., np.newaxis]
amask = np.array([[m['action_mask'][m['turn'][0]]] for m in moments])
players_list = [[m['turn'][0]] for m in moments]
else:
obs = [[replace_none(m['observation'][player], obs_zeros) for player in players] for m in moments]
prob = np.array([[[replace_none(m['selected_prob'][player], 1.0)] for player in players] for m in moments])
act = np.array([[replace_none(m['action'][player], 0) for player in players] for m in moments], dtype=np.int64)[..., np.newaxis]
amask = np.array([[replace_none(m['action_mask'][player], amask_zeros + 1e32) for player in players] for m in moments])
players_list = [players for m in moments]

obs = [[replace_none(m['observation'][player], obs_zeros) for player in players_] for m, players_ in zip(moments, players_list)]
prob = np.array([[[replace_none(m['selected_prob'][player], 1.0)] for player in players_] for m, players_ in zip(moments, players_list)])
act = np.array([[replace_none(m['action'][player], 0) for player in players_] for m, players_ in zip(moments, players_list)], dtype=np.int64)[..., np.newaxis]
amask = np.array([[replace_none(m['action_mask'][player], amask_zeros + 1e32) for player in players_] for m, players_ in zip(moments, players_list)])

# reshape observation
obs = rotate(rotate(obs)) # (T, P, ..., ...) -> (P, ..., T, ...) -> (..., T, P, ...)
Expand Down