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Removing comments and pytests

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This commit is contained in:
Ajay Deshpande
2018-09-12 20:30:09 -07:00
committed by zach dwiel
parent 5a54f67a63
commit 21f8ca3978
1 changed files with 2 additions and 24 deletions
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26
rl_coach/memories/non_episodic/distributed_experience_replay.py
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@@ -51,7 +51,6 @@ class DistributedExperienceReplay(Memory):
super().__init__(max_size)
if max_size[0] != MemoryGranularity.Transitions:
raise ValueError("Experience replay size can only be configured in terms of transitions")
# self.transitions = []
self.allow_duplicates_in_batch_sampling = allow_duplicates_in_batch_sampling
self.db = db
@@ -67,10 +66,8 @@ class DistributedExperienceReplay(Memory):
"""
Get the number of transitions in the ER
"""
# Replace with distributed store len
return self.redis_connection.info(section='keyspace')['db{}'.format(self.db)]['keys']
# return len(self.transitions)
def sample(self, size: int) -> List[Transition]:
"""
Sample a batch of transitions form the replay buffer. If the requested size is larger than the number
@@ -84,8 +81,6 @@ class DistributedExperienceReplay(Memory):
while len(transition_idx) != size:
key = self.redis_connection.randomkey()
transition_idx[key] = pickle.loads(self.redis_connection.get(key))
# transition_idx = np.random.randint(self.num_transitions(), size=size)
else:
if self.num_transitions() >= size:
while len(transition_idx) != size:
@@ -93,12 +88,10 @@ class DistributedExperienceReplay(Memory):
if key in transition_idx:
continue
transition_idx[key] = pickle.loads(self.redis_connection.get(key))
# transition_idx = np.random.choice(self.num_transitions(), size=size, replace=False)
else:
raise ValueError("The replay buffer cannot be sampled since there are not enough transitions yet. "
"There are currently {} transitions".format(self.num_transitions()))
# Replace with distributed store
batch = transition_idx.values()
return batch
@@ -125,10 +118,7 @@ class DistributedExperienceReplay(Memory):
locks and then calls store with lock = True
:return: None
"""
# Replace with distributed store
self.redis_connection.set(uuid.uuid4(), pickle.dumps(transition))
# self.transitions.append(transition)
self._enforce_max_length()
def get_transition(self, transition_index: int, lock: bool=True) -> Union[None, Transition]:
@@ -138,8 +128,6 @@ class DistributedExperienceReplay(Memory):
:param lock: use write locking if this is a shared memory
:return: the corresponding transition
"""
# Replace with distributed store
import pytest; pytest.set_trace()
return pickle.loads(self.redis_connection.get(transition_index))
def remove_transition(self, transition_index: int, lock: bool=True) -> None:
@@ -151,8 +139,6 @@ class DistributedExperienceReplay(Memory):
:param transition_index: the index of the transition to remove
:return: None
"""
# Replace with distributed store
import pytest; pytest.set_trace()
self.redis_connection.delete(transition_index)
# for API compatibility
@@ -162,8 +148,6 @@ class DistributedExperienceReplay(Memory):
:param transition_index: the index of the transition to return
:return: the corresponding transition
"""
# Replace with distributed store
import pytest; pytest.set_trace()
return self.get_transition(transition_index, lock)
# for API compatibility
@@ -173,8 +157,6 @@ class DistributedExperienceReplay(Memory):
:param transition_index: the index of the transition to remove
:return: None
"""
# Replace with distributed store
import pytest; pytest.set_trace()
self.remove_transition(transition_index, lock)
def clean(self, lock: bool=True) -> None:
@@ -182,7 +164,6 @@ class DistributedExperienceReplay(Memory):
Clean the memory by removing all the episodes
:return: None
"""
import pytest; pytest.set_trace()
self.redis_connection.flushall()
# self.transitions = []
@@ -191,9 +172,6 @@ class DistributedExperienceReplay(Memory):
Get the mean reward in the replay buffer
:return: the mean reward
"""
# Replace with distributed store
import pytest; pytest.set_trace()
mean = np.mean([pickle.loads(self.redis_connection.get(key)).reward for key in self.redis_connection.keys()])
# mean = np.mean([transition.reward for transition in self.transitions])
return mean