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introduce graph_manager.phase_context; make sure that calls to graph_manager.train automatically set training phase

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Zach Dwiel
2018-10-02 16:19:49 -04:00
committed by zach dwiel
parent 7382a142bb
commit 517aac163a
3 changed files with 94 additions and 46 deletions
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56
rl_coach/graph_managers/graph_manager.py
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@@ -20,6 +20,7 @@ import time
from collections import OrderedDict
from distutils.dir_util import copy_tree, remove_tree
from typing import List, Tuple
import contextlib
from rl_coach.base_parameters import iterable_to_items, TaskParameters, DistributedTaskParameters, \
VisualizationParameters, \
@@ -285,6 +286,13 @@ class GraphManager(object):
for environment in self.environments:
environment.phase = val
@contextlib.contextmanager
def phase_context(self, phase):
old_phase = self.phase
self.phase = phase
yield
self.phase = old_phase
def set_session(self, sess) -> None:
"""
Set the deep learning framework session for all the modules in the graph
@@ -301,20 +309,17 @@ class GraphManager(object):
self.verify_graph_was_created()
if steps.num_steps > 0:
self.phase = RunPhase.HEATUP
screen.log_title("{}: Starting heatup".format(self.name))
self.heatup_start_time = time.time()
with self.phase_context(RunPhase.HEATUP):
screen.log_title("{}: Starting heatup".format(self.name))
self.heatup_start_time = time.time()
# reset all the levels before starting to heatup
self.reset_internal_state(force_environment_reset=True)
# reset all the levels before starting to heatup
self.reset_internal_state(force_environment_reset=True)
# act for at least steps, though don't interrupt an episode
count_end = self.total_steps_counters[self.phase][EnvironmentSteps] + steps.num_steps
while self.total_steps_counters[self.phase][steps.__class__] < count_end:
self.act(steps, continue_until_game_over=True, return_on_game_over=True)
# training phase
self.phase = RunPhase.UNDEFINED
# act for at least steps, though don't interrupt an episode
count_end = self.total_steps_counters[self.phase][EnvironmentSteps] + steps.num_steps
while self.total_steps_counters[self.phase][steps.__class__] < count_end:
self.act(steps, continue_until_game_over=True, return_on_game_over=True)
def handle_episode_ended(self) -> None:
"""
@@ -333,8 +338,8 @@ class GraphManager(object):
"""
self.verify_graph_was_created()
[manager.train() for manager in self.level_managers]
with self.phase_context(RunPhase.TRAIN):
[manager.train() for manager in self.level_managers]
def reset_internal_state(self, force_environment_reset=False) -> None:
"""
@@ -417,18 +422,17 @@ class GraphManager(object):
# perform several steps of training interleaved with acting
if steps.num_steps > 0:
self.phase = RunPhase.TRAIN
self.reset_internal_state(force_environment_reset=True)
# TODO - the below while loop should end with full episodes, so to avoid situations where we have partial
# episodes in memory
count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps
while self.total_steps_counters[self.phase][steps.__class__] < count_end:
# The actual steps being done on the environment are decided by the agents themselves.
# This is just an high-level controller.
self.act(EnvironmentSteps(1))
self.train()
self.occasionally_save_checkpoint()
self.phase = RunPhase.UNDEFINED
with self.phase_context(RunPhase.TRAIN):
self.reset_internal_state(force_environment_reset=True)
#TODO - the below while loop should end with full episodes, so to avoid situations where we have partial
# episodes in memory
count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps
while self.total_steps_counters[self.phase][steps.__class__] < count_end:
# The actual steps being done on the environment are decided by the agents themselves.
# This is just an high-level controller.
self.act(EnvironmentSteps(1))
self.train()
self.occasionally_save_checkpoint()
def sync_graph(self) -> None:
"""