def move(self, environment):
BaseDDQNGameModel.move(self, environment)
if np.random.rand() < 0.01:
action_vector = random.randrange(self.action_space)
else:
state = environment.state()
q_values = self.ddqn.predict(np.expand_dims(
np.asarray(state).astype(np.float64), axis=0),
batch_size=1)
action_vector = Action.action_from_vector(np.argmax(q_values[0]))
return Action.normalized_action(environment.snake_action,
action_vector)
def _ddqn(self,
total_step_limit=10000000,
total_run_limit=None,
clip=True):
run = 0
total_step = 0
scores = []
while True:
if total_run_limit is not None and run >= total_run_limit:
print "Reached total run limit of: " + str(total_run_limit)
exit(0)
run += 1
env = self.prepare_training_environment()
current_state = env.state()
step = 0
score = env.reward()
while True:
if total_step >= total_step_limit:
print "Reached total step limit of: " + str(
total_step_limit)
exit(0)
total_step += 1
step += 1
action = self._predict_move(current_state)
action_vector = Action.action_from_vector(action)
normalized_action = Action.normalized_action(
env.snake_action, action_vector)
next_state, reward, terminal = env.full_step(normalized_action)
if clip:
np.sign(reward)
score += reward
self._remember(current_state, action, reward, next_state,
terminal)
current_state = next_state
self._step_update(total_step)
if terminal:
scores.append(score)
if len(scores) % SCORE_LOGGING_FREQUENCY == 0:
self.log_score(mean(scores))
print('{{"metric": "score", "value": {}}}'.format(
mean(scores)))
print('{{"metric": "run", "value": {}}}'.format(run))
scores = []
break
