def step(self, action, **kwargs):
r = 0.0
merge_info = {}
for k in range(self.frame_skip):
self.frame_count += 1
obs, reward, done, info = self.env.step(action, **kwargs)
r += reward
for key in info.keys():
if 'reward' in key:
# to assure that we don't igonre other reward
# if new reward was added, consider its logic here
assert (key == 'shaping_reward') or (
key == 'env_reward') or (key == 'x_offset_reward')
merge_info[key] = merge_info.get(key, 0.0) + info[key]
else:
merge_info[key] = info[key]
if info['target_changed']:
logger.warn("[FrameSkip] early break since target was changed")
break
if done:
break
merge_info['frame_count'] = self.frame_count
return obs, r, done, merge_info
def load(self, pathname):
data = np.load(pathname)
other = data['other']
if int(other[0]) > self.max_size:
logger.warn('loading from a bigger size rpm!')
self._curr_size = min(int(other[0]), self.max_size)
self._curr_pos = min(int(other[1]), self.max_size - 1)
self.obs[:self._curr_size] = data['obs'][:self._curr_size]
self.action[:self._curr_size] = data['action'][:self._curr_size]
self.reward[:self._curr_size] = data['reward'][:self._curr_size]
self.terminal[:self._curr_size] = data['terminal'][:self._curr_size]
self.next_obs[:self._curr_size] = data['next_obs'][:self._curr_size]
logger.info("[load rpm]memory loade from {}".format(pathname))
def __init__(self, args):
if machine_info.is_gpu_available():
assert get_gpu_count() == 1, 'Only support training in single GPU,\
Please set environment variable: `export CUDA_VISIBLE_DEVICES=[GPU_ID_TO_USE]` .'
else:
cpu_num = os.environ.get('CPU_NUM')
assert cpu_num is not None and cpu_num == '1', 'Only support training in single CPU,\
Please set environment variable: `export CPU_NUM=1`.'
model = OpenSimModel(OBS_DIM, VEL_DIM, ACT_DIM)
algorithm = parl.algorithms.DDPG(
model,
gamma=GAMMA,
tau=TAU,
actor_lr=ACTOR_LR,
critic_lr=CRITIC_LR)
self.agent = OpenSimAgent(algorithm, OBS_DIM, ACT_DIM)
self.evaluate_result = []
self.lock = threading.Lock()
self.model_lock = threading.Lock()
self.model_queue = queue.Queue()
self.best_shaping_reward = 0
self.best_env_reward = 0
if args.offline_evaluate:
self.offline_evaluate()
else:
t = threading.Thread(target=self.online_evaluate)
t.start()
with self.lock:
while True:
model_path = self.model_queue.get()
if not args.offline_evaluate:
# online evaluate
while not self.model_queue.empty():
model_path = self.model_queue.get()
try:
self.agent.restore(model_path)
break
except Exception as e:
logger.warn("Agent restore Exception: {} ".format(e))
self.cur_model = model_path
self.create_actors()
def step(self, action, **kwargs):
self.step_count += 1
obs, r, done, info = self.env.step(action, **kwargs)
info = self.reward_shaping(obs, r, done, action)
if info['target_vel'] > 2.75:
rate = math.sqrt((2.75**2) / (info['target_vel']**2))
logger.warn('Changing targets, origin targets: {}'.format(
obs['target_vel']))
obs['target_vel'][0] = obs['target_vel'][0] * rate
obs['target_vel'][2] = obs['target_vel'][2] * rate
logger.warn('Changing targets, new targets: {}'.format(
obs['target_vel']))
info['target_vel'] = 2.75
if info['target_vel'] < -0.25:
rate = math.sqrt(((-0.25)**2) / (info['target_vel']**2))
logger.warn('Changing targets, origin targets: {}'.format(
obs['target_vel']))
obs['target_vel'][0] = obs['target_vel'][0] * rate
obs['target_vel'][2] = obs['target_vel'][2] * rate
logger.warn('Changing targets, new targets: {}'.format(
obs['target_vel']))
info['target_vel'] = -0.25
delta = 0
if self.last_target_vel is not None:
delta = np.absolute(
np.array(self.last_target_vel) - np.array(obs['target_vel']))
if (self.last_target_vel is None) or np.all(delta < 1e-5):
info['target_changed'] = False
else:
info['target_changed'] = True
logger.info("[env_wrapper] target_changed, vx:{} vz:{}".format(
obs['target_vel'][0], obs['target_vel'][2]))
self.last_target_change_step = self.step_count
self.target_change_times += 1
info['target_change_times'] = self.target_change_times
self.last_target_vel = obs['target_vel']
assert 'shaping_reward' in info
timeout = False
if self.step_count >= MAXTIME_LIMIT:
timeout = True
if done and not timeout:
# penalty for falling down
info['shaping_reward'] += FALL_PENALTY
info['timeout'] = timeout
self.pre_state_desc = obs
return obs, r, done, info
def _parse_memory(self, actor_state):
mem = actor_state.memory
n = len(mem)
episode_shaping_reward = np.sum(
[exp.info['shaping_reward'] for exp in mem])
episode_env_reward = np.sum([exp.info['env_reward'] for exp in mem])
with self.lock:
if actor_state.model_name == self.cur_model:
self.evaluate_result.append({
'shaping_reward':
episode_shaping_reward,
'env_reward':
episode_env_reward,
'episode_length':
mem[-1].info['frame_count'],
'falldown':
not mem[-1].info['timeout'],
})
logger.info('{}, finish_cnt: {}'.format(
self.cur_model, len(self.evaluate_result)))
logger.info('{}'.format(self.evaluate_result[-1]))
if len(self.evaluate_result) >= args.evaluate_times:
mean_value = {}
for key in self.evaluate_result[0].keys():
mean_value[key] = np.mean(
[x[key] for x in self.evaluate_result])
logger.info('Model: {}, mean_value: {}'.format(
self.cur_model, mean_value))
eval_num = len(self.evaluate_result)
falldown_num = len(
[x for x in self.evaluate_result if x['falldown']])
falldown_rate = falldown_num / eval_num
logger.info('Falldown rate: {}'.format(falldown_rate))
for key in self.evaluate_result[0].keys():
mean_value[key] = np.mean([
x[key] for x in self.evaluate_result
if not x['falldown']
])
logger.info(
'Model: {}, Exclude falldown, mean_value: {}'.format(
self.cur_model, mean_value))
if mean_value['shaping_reward'] > self.best_shaping_reward:
self.best_shaping_reward = mean_value['shaping_reward']
copy2(self.cur_model, './model_zoo')
logger.info(
"[best shaping reward updated:{}] path:{}".format(
self.best_shaping_reward, self.cur_model))
if mean_value[
'env_reward'] > self.best_env_reward and falldown_rate < 0.3:
self.best_env_reward = mean_value['env_reward']
copy2(self.cur_model, './model_zoo')
logger.info(
"[best env reward updated:{}] path:{}, falldown rate: {}"
.format(self.best_env_reward, self.cur_model,
falldown_num / eval_num))
self.evaluate_result = []
while True:
model_path = self.model_queue.get()
if not args.offline_evaluate:
# online evaluate
while not self.model_queue.empty():
model_path = self.model_queue.get()
try:
self.agent.restore(model_path)
break
except Exception as e:
logger.warn(
"Agent restore Exception: {} ".format(e))
self.cur_model = model_path
else:
actor_state.model_name = self.cur_model
actor_state.reset()
