def deep_DiffActInSameTime_callback(locals, globals):
#pprint.pprint(locals)
global max_mean_reward, last_filename
if ('done' in locals and locals['done'] == True):
if ('mean_100ep_reward' in locals and locals['num_episodes'] >=
10 #should be mean_10ep_reward rather than 100
and locals['mean_100ep_reward'] > max_mean_reward):
print("mean_100ep_reward : %s max_mean_reward : %s" %
(locals['mean_100ep_reward'], max_mean_reward))
if (not os.path.exists(
os.path.join(PROJ_DIR,
'models/deepq_actSeparateWith4Directions/'))):
try:
os.mkdir(os.path.join(PROJ_DIR, 'models/'))
except Exception as e:
print(str(e))
try:
os.mkdir(
os.path.join(
PROJ_DIR,
'models/deepq_actSeparateWith4Directions/'))
except Exception as e:
print(str(e))
if (last_filename != ""):
os.remove(last_filename)
print("delete last model file : %s" % last_filename)
max_mean_reward = locals['mean_100ep_reward']
act_x = deepq_mineral_shards.ActWrapper(locals['act_x'])
act_y = deepq_mineral_shards.ActWrapper(locals['act_y'])
filename = os.path.join(
PROJ_DIR,
'models/deepq_actSeparateWith4Directions/mineral_x_%s.pkl' %
locals['mean_100ep_reward'])
act_x.save(filename)
filename = os.path.join(
PROJ_DIR,
'models/deepq_actSeparateWith4Directions/mineral_y_%s.pkl' %
locals['mean_100ep_reward'])
act_y.save(filename)
print("save best mean_100ep_reward model to %s" % filename)
last_filename = filename
def deepq_callback(locals, globals):
global max_mean_reward, last_filename
if 'done' in locals and locals['done'] == True:
if ('mean_100ep_reward' in locals and locals['num_episodes'] >= 10
and locals['mean_100ep_reward'] > max_mean_reward):
print("mean_100ep_reward : %s max_mean_reward : %s" %
(locals['mean_100ep_reward'], max_mean_reward))
if not os.path.exists(os.path.join(PROJ_DIR, 'models/deepq/')):
try:
os.mkdir(os.path.join(PROJ_DIR, 'models/'))
except Exception as e:
print(str(e))
try:
os.mkdir(os.path.join(PROJ_DIR, 'models/deepq/'))
except Exception as e:
print(str(e))
if last_filename != "":
os.remove(last_filename)
print("delete last model file : %s" % last_filename)
max_mean_reward = locals['mean_100ep_reward']
act_x = deepq_mineral_shards.ActWrapper(locals['act_x'])
act_y = deepq_mineral_shards.ActWrapper(locals['act_y'])
filename = os.path.join(
PROJ_DIR,
'models/deepq/mineral_x_%s.pkl' % locals['mean_100ep_reward'])
act_x.save(filename)
filename = os.path.join(
PROJ_DIR,
'models/deepq/mineral_y_%s.pkl' % locals['mean_100ep_reward'])
act_y.save(filename)
print("save best mean_100ep_reward model to %s" % filename)
last_filename = filename