def setup_space(self):
self.observation_space = spaces.Box(low=0,
high=config.ss_state_max_queue,
shape=(config.ss_num_ports,
config.ss_num_ports))
self.action_space = spaces.Discrete(math.factorial(
config.ss_num_ports))
def setup_space(self):
# Set up the observation and action space
# The boundary of the space may change if the dynamics is changed
# a warning message will show up every time e.g., the observation falls
# out of the observation space
self.obs_low = np.array([0] * (config.num_servers + 1))
self.obs_high = np.array([config.load_balance_obs_high] * (config.num_servers + 1))
self.observation_space = spaces.Box(
low=self.obs_low, high=self.obs_high, dtype=np.float32)
self.action_space = spaces.Discrete(config.num_servers)
def setup_space(self):
# Set up the observation and action space
# The boundary of the space may change if the dynamics is changed
# a warning message will show up every time e.g., the observation falls
# out of the observation space
self.obs_low = np.array([0] * 11)
self.obs_high = np.array([
10e6, 100, 100, 500, 5, 10e6, 10e6, 10e6, 10e6, 10e6, 10e6])
self.observation_space = spaces.Box(
low=self.obs_low, high=self.obs_high, dtype=np.float32)
self.action_space = spaces.Discrete(6)
def __init__(self, seed=42):
self.seed(seed)
self.cache_size = config.cache_size
# load trace, attach initial online feature values
self.src = TraceSrc(trace=config.cache_trace, cache_size=self.cache_size)
# set up the state and action space
self.action_space = spaces.Discrete(2)
self.observation_space = spaces.Box(self.src.min_values, \
self.src.max_values, \
dtype=np.float32)
# cache simulator
self.sim = CacheSim(cache_size=self.cache_size, \
policy='lru', \
action_space=self.action_space, \
state_space=self.observation_space)
# reset environment (generate new jobs)
self.reset(1, 2)