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import logging | |
import gym | |
from gym import spaces | |
import numpy as np | |
import pwnagotchi.ai.featurizer as featurizer | |
import pwnagotchi.ai.reward as reward | |
from pwnagotchi.ai.parameter import Parameter | |
class Environment(gym.Env): | |
metadata = {'render.modes': ['human']} | |
params = [ | |
Parameter('min_rssi', min_value=-200, max_value=-50), | |
Parameter('ap_ttl', min_value=30, max_value=600), | |
Parameter('sta_ttl', min_value=60, max_value=300), | |
Parameter('recon_time', min_value=5, max_value=60), | |
Parameter('max_inactive_scale', min_value=3, max_value=10), | |
Parameter('recon_inactive_multiplier', min_value=1, max_value=3), | |
Parameter('hop_recon_time', min_value=5, max_value=60), | |
Parameter('min_recon_time', min_value=1, max_value=30), | |
Parameter('max_interactions', min_value=1, max_value=25), | |
Parameter('max_misses_for_recon', min_value=3, max_value=10), | |
Parameter('excited_num_epochs', min_value=5, max_value=30), | |
Parameter('bored_num_epochs', min_value=5, max_value=30), | |
Parameter('sad_num_epochs', min_value=5, max_value=30), | |
] | |
def __init__(self, agent, epoch): | |
super(Environment, self).__init__() | |
self._agent = agent | |
self._epoch = epoch | |
self._epoch_num = 0 | |
self._last_render = None | |
# see https://github.com/evilsocket/pwnagotchi/issues/583 | |
self._supported_channels = agent.supported_channels() | |
self._extended_spectrum = any(ch > 140 for ch in self._supported_channels) | |
self._histogram_size, self._observation_shape = featurizer.describe(self._extended_spectrum) | |
Environment.params += [ | |
Parameter('_channel_%d' % ch, min_value=0, max_value=1, meta=ch + 1) for ch in | |
range(self._histogram_size) if ch + 1 in self._supported_channels | |
] | |
self.last = { | |
'reward': 0.0, | |
'observation': None, | |
'policy': None, | |
'params': {}, | |
'state': None, | |
'state_v': None | |
} | |
self.action_space = spaces.MultiDiscrete([p.space_size() for p in Environment.params if p.trainable]) | |
self.observation_space = spaces.Box(low=0, high=1, shape=self._observation_shape, dtype=np.float32) | |
self.reward_range = reward.range | |
@staticmethod | |
def policy_size(): | |
return len(list(p for p in Environment.params if p.trainable)) | |
@staticmethod | |
def policy_to_params(policy): | |
num = len(policy) | |
params = {} | |
assert len(Environment.params) == num | |
channels = [] | |
for i in range(num): | |
param = Environment.params[i] | |
if '_channel' not in param.name: | |
params[param.name] = param.to_param_value(policy[i]) | |
else: | |
has_chan = param.to_param_value(policy[i]) | |
# print("%s policy:%s bool:%s" % (param.name, policy[i], has_chan)) | |
chan = param.meta | |
if has_chan: | |
channels.append(chan) | |
params['channels'] = channels | |
return params | |
def _next_epoch(self): | |
logging.debug("[ai] waiting for epoch to finish ...") | |
return self._epoch.wait_for_epoch_data() | |
def _apply_policy(self, policy): | |
new_params = Environment.policy_to_params(policy) | |
self.last['policy'] = policy | |
self.last['params'] = new_params | |
self._agent.on_ai_policy(new_params) | |
def step(self, policy): | |
# create the parameters from the policy and update | |
# update them in the algorithm | |
self._apply_policy(policy) | |
self._epoch_num += 1 | |
# wait for the algorithm to run with the new parameters | |
state = self._next_epoch() | |
self.last['reward'] = state['reward'] | |
self.last['state'] = state | |
self.last['state_v'] = featurizer.featurize(state, self._epoch_num) | |
self._agent.on_ai_step() | |
return self.last['state_v'], self.last['reward'], not self._agent.is_training(), {} | |
def reset(self): | |
# logging.info("[ai] resetting environment ...") | |
self._epoch_num = 0 | |
state = self._next_epoch() | |
self.last['state'] = state | |
self.last['state_v'] = featurizer.featurize(state, 1) | |
return self.last['state_v'] | |
def _render_histogram(self, hist): | |
for ch in range(self._histogram_size): | |
if hist[ch]: | |
logging.info(" CH %d: %s" % (ch + 1, hist[ch])) | |
def render(self, mode='human', close=False, force=False): | |
# when using a vectorialized environment, render gets called twice | |
# avoid rendering the same data | |
if self._last_render == self._epoch_num: | |
return | |
if not self._agent.is_training() and not force: | |
return | |
self._last_render = self._epoch_num | |
logging.info("[ai] --- training epoch %d/%d ---" % (self._epoch_num, self._agent.training_epochs())) | |
logging.info("[ai] REWARD: %f" % self.last['reward']) | |
logging.debug("[ai] policy: %s" % ', '.join("%s:%s" % (name, value) for name, value in self.last['params'].items())) | |
logging.info("[ai] observation:") | |
for name, value in self.last['state'].items(): | |
if 'histogram' in name: | |
logging.info(" %s" % name.replace('_histogram', '')) | |
self._render_histogram(value) |