def __init__(self, config: Dict[str, any], result_dir: str, cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
# evaluation specific variables
self.observation_seen = 0
self.episode_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(self._observe_expired_incomplete_experience)
self.experimental_reward = config.get('experimental_reward', False)
agent_config = config['agent_config']
self.converter = CachingStrategyRLConverter()
# action space: should cache: true or false
# state space: [capacity (1), query key(1), query result set(num_indexes)]
fields_in_state = len(CachingAgentSystemState.__slots__)
self.agent = Agent.from_spec(agent_config,
state_space=FloatBox(shape=(fields_in_state,)),
action_space=IntBox(2))
self.logger = logging.getLogger(__name__)
name = 'rl_caching_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger', result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger', result_dir=self.result_dir)
self.observation_logger = create_file_logger(name=f'{name}_observation_logger', result_dir=self.result_dir)
self.entry_hits_logger = create_file_logger(name=f'{name}_entry_hits_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
def __init__(self, config: Dict[str, any], result_dir: str,
cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
self.supported_observations = {
ObservationType.Hit, ObservationType.Miss,
ObservationType.Invalidate
}
# evaluation specific variables
self.observation_seen = 0
self.cum_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(
self._observe_expiry_eviction)
self.non_terminal_observations = {
ObservationType.EvictionPolicy, ObservationType.Expiration
}
agent_config = config['agent_config']
self.maximum_ttl = config['max_ttl']
fields_in_state = len(MultiTaskAgentSystemState.__slots__)
action_space = RLDict({
'ttl': IntBox(low=0, high=self.maximum_ttl),
'eviction': IntBox(low=0, high=2)
})
self.agent = Agent.from_spec(
agent_config,
state_space=FloatBox(shape=(fields_in_state, )),
action_space=action_space)
# TODO refactor into common RL interface for all strategies
self.logger = logging.getLogger(__name__)
name = 'rl_multi_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger',
result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger',
result_dir=self.result_dir)
self.ttl_logger = create_file_logger(name=f'{name}_ttl_logger',
result_dir=self.result_dir)
self.observation_logger = create_file_logger(
name=f'{name}_observation_logger', result_dir=self.result_dir)
self.performance_logger = create_file_logger(
name=f'{name}_performance_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
def __init__(self, config: Dict[str, any], result_dir: str,
cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
self.observation_seen = 0
self.cum_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(
self._observe_expiry_eviction)
self.non_terminal_observations = {
ObservationType.EvictionPolicy, ObservationType.Expiration
}
agent_config = config['agent_config']
self.maximum_ttl = config['max_ttl']
self.experimental_reward = config.get('experimental_reward', False)
fields_in_state = len(TTLAgentSystemState.__slots__)
self.agent = Agent.from_spec(
agent_config,
state_space=FloatBox(shape=(fields_in_state, )),
action_space=FloatBox(low=0, high=self.maximum_ttl, shape=(1, )))
# TODO refactor into common RL interface for all strategies
self.logger = logging.getLogger(__name__)
name = 'rl_ttl_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger',
result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger',
result_dir=self.result_dir)
self.ttl_logger = create_file_logger(name=f'{name}_ttl_logger',
result_dir=self.result_dir)
self.observation_logger = create_file_logger(
name=f'{name}_observation_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
self.errors = create_file_logger(name=f'{name}_error_logger',
result_dir=self.result_dir)
def __init__(self, config: Dict[str, any], result_dir: str,
cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
# evaluation specific variables
self.observation_seen = 0
self.episode_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(
self._observe_expired_incomplete_experience)
self.view_of_the_cache = {} # type: Dict[str, Dict[str, any]]
self._end_episode_observation = {
ObservationType.Invalidate, ObservationType.Miss,
ObservationType.Expiration
}
# TODO refactor into common RL interface for all strategies
# Agent configuration (can be shared with others)
agent_config = config['agent_config']
fields_in_state = len(EvictionAgentSystemState.__slots__)
self.converter = EvictionStrategyRLConverter(self.result_dir)
# State: fields to observe in question
# Action: to evict or not that key
self.agent = Agent.from_spec(
agent_config,
state_space=FloatBox(shape=(fields_in_state, )),
action_space=IntBox(low=0, high=2))
self.logger = logging.getLogger(__name__)
name = 'rl_eviction_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger',
result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger',
result_dir=self.result_dir)
self.observation_logger = create_file_logger(
name=f'{name}_observation_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
def __init__(self, result_dir: str):
self.vocabulary = Vocabulary(add_pad=True, add_unk=False)
self.logger = logging.getLogger(__name__)
name = 'rl_eviction_strategy'
self.performance_logger = create_file_logger(
name=f'{name}_performance_logger', result_dir=result_dir)
def __init__(self):
self.vocabulary = Vocabulary()
self.logger = logging.getLogger(__name__)
class RLTtlStrategy(TtlStrategy):
"""RL driven TTL estimation strategy."""
def __init__(self, config: Dict[str, any], result_dir: str,
cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
self.observation_seen = 0
self.cum_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(
self._observe_expiry_eviction)
self.non_terminal_observations = {
ObservationType.EvictionPolicy, ObservationType.Expiration
}
agent_config = config['agent_config']
self.maximum_ttl = config['max_ttl']
self.experimental_reward = config.get('experimental_reward', False)
fields_in_state = len(TTLAgentSystemState.__slots__)
self.agent = Agent.from_spec(
agent_config,
state_space=FloatBox(shape=(fields_in_state, )),
action_space=FloatBox(low=0, high=self.maximum_ttl, shape=(1, )))
# TODO refactor into common RL interface for all strategies
self.logger = logging.getLogger(__name__)
name = 'rl_ttl_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger',
result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger',
result_dir=self.result_dir)
self.ttl_logger = create_file_logger(name=f'{name}_ttl_logger',
result_dir=self.result_dir)
self.observation_logger = create_file_logger(
name=f'{name}_observation_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
self.errors = create_file_logger(name=f'{name}_error_logger',
result_dir=self.result_dir)
def estimate_ttl(self, key: str, values: Dict[str, any],
operation_type: OperationType) -> float:
observation_time = time.time()
encoded_key = self.key_vocab.add_or_get_id(key)
cache_utility = self.cache_stats.cache_utility
state = TTLAgentSystemState(encoded_key=encoded_key,
hit_count=0,
step_code=0,
cache_utility=cache_utility,
operation_type=operation_type.value)
state_as_numpy = state.to_numpy()
agent_action = self.agent.get_action(state_as_numpy)
action = agent_action.item()
incomplete_experience = TTLAgentObservedExperience(
state=state,
agent_action=agent_action,
starting_state=state.copy(),
observation_time=observation_time)
self._incomplete_experiences.set(key, incomplete_experience,
self.maximum_ttl)
return action
def observe(self, key: str, observation_type: ObservationType,
info: Dict[str, any]):
observed_experience = self._incomplete_experiences.get(key)
if observed_experience is None or observation_type == ObservationType.Expiration:
return # haven't had to make a decision on it
current_time = time.time()
stored_state = observed_experience.state
if observation_type == ObservationType.Hit:
stored_state.hit_count += 1
# elif observation_type in self.non_terminal_observations:
# # it was evicted by another policy don't attempt to learn stuff from this
# pass
estimated_ttl = observed_experience.agent_action.item()
first_observation_time = observed_experience.observation_time
real_ttl = current_time - first_observation_time
stored_state.step_code = observation_type.value
stored_state.cache_utility = self.cache_stats.cache_utility
self.reward_agent(observation_type, observed_experience, real_ttl)
if observation_type != ObservationType.Hit:
self.ttl_logger.info(
f'{self.episode_num},{observation_type.name},{key},{estimated_ttl},{real_ttl},{stored_state.hit_count}'
)
self._incomplete_experiences.delete(key)
self.observation_seen += 1
if self.observation_seen % self.checkpoint_steps == 0:
self.logger.info(
f'Observation seen so far: {self.observation_seen}, reward so far: {self.cum_reward}'
)
if observation_type not in self.non_terminal_observations:
self.observation_logger.info(f'{key},{observation_type}')
def _observe_expiry_eviction(self, key: str,
observation_type: ObservationType,
info: Dict[str, any]):
"""Observe decisions taken that hasn't been observed by main cache. e.g. don't cache -> ttl up -> no miss"""
self.observation_logger.info(
f'{self.episode_num},{key},{observation_type}')
experience = info['value'] # type: TTLAgentObservedExperience
self.ttl_logger.info(
f'{self.episode_num},{observation_type.name},{key},{experience.agent_action.item()},'
f'{experience.agent_action.item()},{experience.state.hit_count}')
experience.state.step_code = observation_type.value
self.reward_agent(observation_type, experience,
experience.agent_action.item())
def reward_agent(self, observation_type: ObservationType,
experience: TTLAgentObservedExperience,
real_ttl: time) -> int:
# reward more utilisation of the cache capacity given more hits
final_state = experience.state
difference_in_ttl = -abs((experience.agent_action.item() + 1) /
max(min(real_ttl, self.maximum_ttl), 1))
# reward = final_state.hit_count - abs(difference_in_ttl * self.cache_stats.cache_utility)
if observation_type == ObservationType.Hit:
reward = 1
terminal = False
# elif observation_type == ObservationType.EvictionPolicy:
# reward = 0
# terminal = True
else:
reward = difference_in_ttl
if abs(difference_in_ttl) < 10:
reward = 10
terminal = True
self.logger.debug(
f'Hits: {final_state.hit_count}, ttl diff: {difference_in_ttl}, Reward: {reward}'
)
self.agent.observe(
preprocessed_states=experience.starting_state.to_numpy(),
actions=experience.agent_action,
internals=[],
rewards=reward,
next_states=final_state.to_numpy(),
terminals=terminal)
self.cum_reward += reward
self.reward_logger.info(f'{self.episode_num},{reward}')
loss = self.agent.update()
if loss is not None:
self.loss_logger.info(f'{self.episode_num},{loss[0]}')
return reward
def close(self):
super().close()
for (k, v) in list(self._incomplete_experiences.items()):
self.ttl_logger.info(
f'{self.episode_num},{ObservationType.EndOfEpisode.name},{k},{v.agent_action.item()},'
f'{v.agent_action.item()},{v.state.hit_count}')
self._incomplete_experiences.clear()
try:
self.agent.reset()
except Exception as e:
self.errors.info(e)
class RLMultiTasksStrategy(BaseStrategy):
"""RL driven multi task strategy - Caching, eviction, and ttl estimation."""
def __init__(self, config: Dict[str, any], result_dir: str,
cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
self.supported_observations = {
ObservationType.Hit, ObservationType.Miss,
ObservationType.Invalidate
}
# evaluation specific variables
self.observation_seen = 0
self.cum_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(
self._observe_expiry_eviction)
self.non_terminal_observations = {
ObservationType.EvictionPolicy, ObservationType.Expiration
}
agent_config = config['agent_config']
self.maximum_ttl = config['max_ttl']
fields_in_state = len(MultiTaskAgentSystemState.__slots__)
action_space = RLDict({
'ttl': IntBox(low=0, high=self.maximum_ttl),
'eviction': IntBox(low=0, high=2)
})
self.agent = Agent.from_spec(
agent_config,
state_space=FloatBox(shape=(fields_in_state, )),
action_space=action_space)
# TODO refactor into common RL interface for all strategies
self.logger = logging.getLogger(__name__)
name = 'rl_multi_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger',
result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger',
result_dir=self.result_dir)
self.ttl_logger = create_file_logger(name=f'{name}_ttl_logger',
result_dir=self.result_dir)
self.observation_logger = create_file_logger(
name=f'{name}_observation_logger', result_dir=self.result_dir)
self.performance_logger = create_file_logger(
name=f'{name}_performance_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
def observe(self, key: str, observation_type: ObservationType,
info: Dict[str, any]):
observed_experience = self._incomplete_experiences.get(key)
if observed_experience is None:
return # haven't had to make a decision on it
current_time = time.time()
stored_state = observed_experience.state
stored_state.step_code = observation_type.value
stored_state.cache_utility = self.cache_stats.cache_utility
if observation_type == ObservationType.Hit:
stored_state.hit_count += 1
else:
# Include eviction, invalidation, and miss
estimated_ttl = observed_experience.agent_action['ttl'].item()
first_observation_time = observed_experience.observation_time
real_ttl = current_time - first_observation_time
# log the difference between the estimated ttl and real ttl
self.ttl_logger.info(
f'{self.episode_num},{observation_type.name},{key},{estimated_ttl},{real_ttl},{stored_state.hit_count}'
)
self._incomplete_experiences.delete(key)
self.reward_agent(observation_type, observed_experience)
self.observation_seen += 1
if self.observation_seen % self.checkpoint_steps == 0:
self.logger.info(
f'Observation seen so far: {self.observation_seen}, reward so far: {self.cum_reward}'
)
if observation_type not in self.non_terminal_observations:
self.observation_logger.info(f'{key},{observation_type}')
def trim_cache(self, cache: TTLCache):
# trim cache isn't called often so the operation is ok to be expensive
# produce an action on the whole cache
keys_to_evict = []
for (key,
stored_experience) in list(self._incomplete_experiences.items()):
action = self.agent.get_action(
stored_experience.state.to_numpy())['eviction']
evict = (action.flatten() == 1).item()
if evict:
cache.delete(key)
keys_to_evict.append(key)
# update stored value for eviction action
stored_experience.agent_action['eviction'] = action
stored_experience.manual_eviction = True
if len(keys_to_evict) == 0:
self.logger.error('trim_cache No keys were evicted.')
return keys_to_evict
def should_cache(self, key: str, values: Dict[str, str], ttl: int,
operation_type: OperationType) -> bool:
# cache objects that have TTL more than 1 second (maybe make this configurable?)
return ttl > 10
def estimate_ttl(self, key: str, values: Dict[str, any],
operation_type: OperationType) -> float:
# TODO check if it is in the observed queue
observation_time = time.time()
encoded_key = self.key_vocab.add_or_get_id(key)
cache_utility = self.cache_stats.cache_utility
state = MultiTaskAgentSystemState(encoded_key=encoded_key,
hit_count=0,
ttl=0,
step_code=0,
cache_utility=cache_utility,
operation_type=operation_type.value)
state_as_numpy = state.to_numpy()
agent_action = self.agent.get_action(state_as_numpy)
action = agent_action['ttl'].item()
incomplete_experience = MultiTaskAgentObservedExperience(
state=state,
agent_action=agent_action,
starting_state=state.copy(),
observation_time=observation_time,
manual_eviction=False)
self._incomplete_experiences.set(key, incomplete_experience,
self.maximum_ttl)
return action
def reward_agent(self, observation_type: ObservationType,
experience: MultiTaskAgentObservedExperience) -> int:
# reward more utilisation of the cache capacity given more hits
final_state = experience.state
# difference_in_ttl = -abs((experience.agent_action.item() + 1) / min(real_ttl, self.maximum_ttl))
reward = 0
terminal = False
if observation_type == observation_type.Invalidate and (
experience.agent_action['ttl'] < 10 or
(experience.agent_action['eviction'].flatten() == 1).item()):
# if evicted or not cached, followed by an invalidate
reward = 10
terminal = True
elif observation_type == observation_type.Invalidate or observation_type == observation_type.Miss:
reward = -10
terminal = True
elif observation_type == ObservationType.Hit:
terminal = False
reward = 1
if experience.manual_eviction:
if observation_type == observation_type.Expiration:
reward = -10
terminal = True
if observation_type == observation_type.Hit:
reward = 2
self.performance_metric_for_eviction(experience, observation_type)
self.agent.observe(
preprocessed_states=experience.starting_state.to_numpy(),
actions=experience.agent_action,
internals=[],
rewards=terminal,
next_states=final_state.to_numpy(),
terminals=True)
self.cum_reward += reward
self.reward_logger.info(f'{self.episode_num},{reward}')
# TODO use self.agent.update_schedule to decide when to call update
loss = self.agent.update()
if loss is not None:
self.loss_logger.info(f'{self.episode_num},{loss[0]}')
return reward
def _observe_expiry_eviction(self, key: str,
observation_type: ObservationType,
info: Dict[str, any]):
"""Observe decisions taken that hasn't been observed by main cache. e.g. don't cache -> ttl up -> no miss"""
self.observation_logger.info(
f'{self.episode_num},{key},{observation_type}')
experience = info['value'] # type: MultiTaskAgentObservedExperience
self.ttl_logger.info(
f'{self.episode_num},{observation_type.name},{key},{experience.agent_action["ttl"].item()},'
f'{experience.agent_action["ttl"].item()},{experience.state.hit_count}'
)
experience.state.step_code = observation_type.value
self.reward_agent(observation_type, experience)
def performance_metric_for_eviction(
self, stored_experience: MultiTaskAgentObservedExperience,
observation_type: ObservationType) -> int:
should_evict = (
stored_experience.agent_action['eviction'].flatten() == 1).item()
if observation_type == ObservationType.Expiration:
if should_evict:
# reward if should evict didn't observe any follow up miss
self.performance_logger.info(f'{self.episode_num},TrueEvict')
# else didn't evict
else:
# reward for not evicting a key that received more hits.
# or 0 if it didn't evict but also didn't get any hits
gain_for_not_evicting = stored_experience.state.hit_count - stored_experience.starting_state.hit_count
if gain_for_not_evicting > 0:
self.performance_logger.info(
f'{self.episode_num},TrueMiss')
else:
self.performance_logger.info(
f'{self.episode_num},MissEvict')
return gain_for_not_evicting
elif observation_type == ObservationType.Invalidate:
# Set/Delete, remove entry from the cache.
# reward an eviction followed by invalidation.
if should_evict:
self.performance_logger.info(f'{self.episode_num},TrueEvict')
else:
# punish not evicting a key that got invalidated after.
self.performance_logger.info(f'{self.episode_num},MissEvict')
elif observation_type == ObservationType.Miss:
if should_evict:
self.performance_logger.info(f'{self.episode_num},FalseEvict')
# Miss after making an eviction decision
# Punish, a read after an eviction decision
def close(self):
for (k, v) in list(self._incomplete_experiences.items()):
self.ttl_logger.info(
f'{self.episode_num},{ObservationType.EndOfEpisode.name},{k},{v.agent_action["ttl"].item()},'
f'{v.agent_action["ttl"].item()},{v.state.hit_count}')
self.performance_logger.info(f'{self.episode_num},TrueMiss')
super().close()
self._incomplete_experiences.clear()
try:
self.agent.reset()
except Exception as e:
pass
class RLCachingStrategy(CachingStrategy):
def __init__(self, config: Dict[str, any], result_dir: str, cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
# evaluation specific variables
self.observation_seen = 0
self.episode_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(self._observe_expired_incomplete_experience)
self.experimental_reward = config.get('experimental_reward', False)
agent_config = config['agent_config']
self.converter = CachingStrategyRLConverter()
# action space: should cache: true or false
# state space: [capacity (1), query key(1), query result set(num_indexes)]
fields_in_state = len(CachingAgentSystemState.__slots__)
self.agent = Agent.from_spec(agent_config,
state_space=FloatBox(shape=(fields_in_state,)),
action_space=IntBox(2))
self.logger = logging.getLogger(__name__)
name = 'rl_caching_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger', result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger', result_dir=self.result_dir)
self.observation_logger = create_file_logger(name=f'{name}_observation_logger', result_dir=self.result_dir)
self.entry_hits_logger = create_file_logger(name=f'{name}_entry_hits_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
def should_cache(self, key: str, values: Dict[str, str], ttl: int, operation_type: OperationType) -> bool:
# TODO what about the case of a cache key that exist already in the incomplete exp?
assert self._incomplete_experiences.get(key) is None, \
"should_cache is assumed to be first call and key shouldn't be in the cache"
observation_time = time.time()
encoded_key = self.key_vocab.add_or_get_id(key)
state = CachingAgentSystemState(encoded_key=encoded_key,
ttl=ttl,
hit_count=0,
step_code=0,
operation_type=operation_type.value)
agent_action = self.agent.get_action(state.to_numpy())
incomplete_experience_entry = CachingAgentIncompleteExperienceEntry(state=state,
agent_action=agent_action,
starting_state=state.copy(),
observation_time=observation_time)
action = self.converter.agent_to_system_action(agent_action)
self._incomplete_experiences.set(key, incomplete_experience_entry, ttl)
return action
def observe(self, key: str, observation_type: ObservationType, info: Dict[str, any]):
# TODO include stats/capacity information in the info dict
experience = self._incomplete_experiences.get(key) # type: CachingAgentIncompleteExperienceEntry
if experience is None:
return # if I haven't had to make a decision on this, ignore it.
self.observation_logger.info(f'{self.episode_num},{key},{observation_type.name}')
if observation_type == ObservationType.Hit:
experience.state.hit_count += 1
else:
self._reward_experience(key, experience, observation_type)
self.observation_seen += 1
if self.observation_seen % self.checkpoint_steps == 0:
self.logger.info(f'Observation seen so far: {self.observation_seen}, reward so far: {self.episode_reward}')
def _observe_expired_incomplete_experience(self, key: str, observation_type: ObservationType, info: Dict[str, any]):
"""Observe decisions taken that hasn't been observed by main cache. e.g. don't cache -> ttl up -> no miss"""
assert observation_type == ObservationType.Expiration
experience = info['value']
self._reward_experience(key, experience, observation_type)
def _reward_experience(self,
key: str,
experience: CachingAgentIncompleteExperienceEntry,
observation_type: ObservationType):
state = experience.state
state.step_code = observation_type.value
self._incomplete_experiences.delete(key)
self.entry_hits_logger.info(f'{self.episode_num},{key},{experience.state.hit_count}')
reward = self.converter.system_to_agent_reward(experience)
if self.experimental_reward:
# TODO add cache utility to state and reward
pass
self.agent.observe(preprocessed_states=experience.starting_state.to_numpy(),
actions=experience.agent_action,
internals=[],
rewards=reward,
next_states=experience.state.to_numpy(),
terminals=False)
self.episode_reward += reward
self.reward_logger.info(f'{self.episode_num},{reward}')
self.logger.debug(f'Key: {key} is in terminal state because: {str(observation_type)}')
loss = self.agent.update()
if loss is not None:
self.loss_logger.info(f'{self.episode_num},{loss[0]}')
def close(self):
super().close()
self.agent.reset()
self._incomplete_experiences.clear()