def _build_normalizers(self):
self.s_norm = Normalizer(self.get_state_size(), self.world.env.build_state_norm_groups(self.id))
self.s_norm.set_mean_std(-self.world.env.build_state_offset(self.id),
1 / self.world.env.build_state_scale(self.id))
self.g_norm = Normalizer(self.get_goal_size(), self.world.env.build_goal_norm_groups(self.id))
self.g_norm.set_mean_std(-self.world.env.build_goal_offset(self.id),
1 / self.world.env.build_goal_scale(self.id))
self.a_norm = Normalizer(self.world.env.get_action_size())
self.a_norm.set_mean_std(-self.world.env.build_action_offset(self.id),
1 / self.world.env.build_action_scale(self.id))
return
def _build_normalizers(self):
self.s_norm = Normalizer(self.get_state_size(), self.world.env.build_state_norm_groups(self.id))
self.s_norm.set_mean_std(-self.world.env.build_state_offset(self.id),
1 / self.world.env.build_state_scale(self.id))
self.g_norm = Normalizer(self.get_goal_size(), self.world.env.build_goal_norm_groups(self.id))
self.g_norm.set_mean_std(-self.world.env.build_goal_offset(self.id),
1 / self.world.env.build_goal_scale(self.id))
self.a_norm = Normalizer(self.world.env.get_action_size())
self.a_norm.set_mean_std(-self.world.env.build_action_offset(self.id),
1 / self.world.env.build_action_scale(self.id))
return
class RLAgent(ABC):
class Mode(Enum):
TRAIN = 0
TEST = 1
TRAIN_END = 2
NAME = "None"
ITERS_PER_UPDATE = "ItersPerUpdate"
DISCOUNT_KEY = "Discount"
MINI_BATCH_SIZE_KEY = "MiniBatchSize"
REPLAY_BUFFER_SIZE_KEY = "ReplayBufferSize"
INIT_SAMPLES_KEY = "InitSamples"
NORMALIZER_SAMPLES_KEY = "NormalizerSamples"
OUTPUT_ITERS_KEY = "OutputIters"
INT_OUTPUT_ITERS_KEY = "IntOutputIters"
TEST_EPISODES_KEY = "TestEpisodes"
EXP_ANNEAL_SAMPLES_KEY = "ExpAnnealSamples"
EXP_PARAM_BEG_KEY = "ExpParamsBeg"
EXP_PARAM_END_KEY = "ExpParamsEnd"
def __init__(self, world, id, json_data):
self.world = world
self.id = id
self.logger = Logger()
self._mode = self.Mode.TRAIN
assert self._check_action_space(), \
Logger.print("Invalid action space, got {:s}".format(str(self.get_action_space())))
self._enable_training = True
self.path = Path()
self.iter = int(0)
self.start_time = time.time()
self.iters_per_update = int(1)
self.discount = 0.95
self.mini_batch_size = int(32)
self.replay_buffer_size = int(50000)
self.init_samples = int(1000)
self.normalizer_samples = np.inf
self._local_mini_batch_size = self.mini_batch_size # batch size for each work for multiprocessing
self._need_normalizer_update = True
self._total_sample_count = 0
self._output_dir = ""
self._int_output_dir = ""
self.output_iters = 100
self.int_output_iters = 100
self.train_return = 0.0
self.test_episodes = int(0)
self.test_episode_count = int(0)
self.test_return = 0.0
self.avg_test_return = 0.0
self.exp_anneal_samples = 320000
self.exp_params_beg = ExpParams()
self.exp_params_end = ExpParams()
self.exp_params_curr = ExpParams()
self._load_params(json_data)
self._build_replay_buffer(self.replay_buffer_size)
self._build_normalizers()
self._build_bounds()
self.reset()
return
def __str__(self):
action_space_str = str(self.get_action_space())
info_str = ""
info_str += '" ID": {:d},\n "Type": "{:s}",\n "ActionSpace": "{:s}",\n "StateDim": {:d},\n "GoalDim": {:d},\n "ActionDim": {:d}'.format(
self.id, self.NAME,
action_space_str[action_space_str.rfind('.') + 1:],
self.get_state_size(), self.get_goal_size(),
self.get_action_size())
return "{\n" + info_str + "\n}"
def get_output_dir(self):
return self._output_dir
def set_output_dir(self, out_dir):
self._output_dir = out_dir
if (self._output_dir != ""):
self.logger.configure_output_file(out_dir + "/agent" +
str(self.id) + "_log.txt")
return
output_dir = property(get_output_dir, set_output_dir)
def get_int_output_dir(self):
return self._int_output_dir
def set_int_output_dir(self, out_dir):
self._int_output_dir = out_dir
return
int_output_dir = property(get_int_output_dir, set_int_output_dir)
def reset(self):
self.path.clear()
return
def update(self, timestep):
if self.need_new_action():
self._update_new_action()
return
def end_episode(self):
if (self.path.pathlength() > 0):
self._end_path()
if (self._mode == self.Mode.TRAIN):
if (self.enable_training and self.path.pathlength() > 0):
self._store_path(self.path)
if (self._valid_train_step()):
self._train()
self._update_exp_params()
self.world.env.set_sample_count(
self._total_sample_count)
elif (self._mode == self.Mode.TEST):
self._update_test_return(self.path)
else:
assert False, Logger.print("Unsupported RL agent mode" +
str(self._mode))
return
def has_goal(self):
return self.get_goal_size() > 0
def predict_val(self):
return 0
def get_enable_training(self):
return self._enable_training
def set_enable_training(self, enable):
self._enable_training = enable
if (self._enable_training):
self.reset()
return
enable_training = property(get_enable_training, set_enable_training)
def enable_testing(self):
return self.test_episodes > 0
def get_name(self):
return self.NAME
@abstractmethod
def save_model(self, out_path):
pass
@abstractmethod
def load_model(self, in_path):
pass
@abstractmethod
def _decide_action(self, s, g):
pass
@abstractmethod
def _get_output_path(self):
pass
@abstractmethod
def _get_int_output_path(self):
pass
@abstractmethod
def _train_step(self):
pass
@abstractmethod
def _check_action_space(self):
pass
def get_action_space(self):
return self.world.env.get_action_space(self.id)
def get_state_size(self):
return self.world.env.get_state_size(self.id)
def get_goal_size(self):
return self.world.env.get_goal_size(self.id)
def get_action_size(self):
return self.world.env.get_action_size(self.id)
def get_num_actions(self):
return self.world.env.get_num_actions(self.id)
def need_new_action(self):
return self.world.env.need_new_action(self.id)
def _build_normalizers(self):
self._s_norm = Normalizer(
self.get_state_size(),
self.world.env.build_state_norm_groups(self.id))
self._s_norm.set_mean_std(
-self.world.env.build_state_offset(self.id),
1 / self.world.env.build_state_scale(self.id))
self._g_norm = Normalizer(
self.get_goal_size(),
self.world.env.build_goal_norm_groups(self.id))
self._g_norm.set_mean_std(-self.world.env.build_goal_offset(self.id),
1 / self.world.env.build_goal_scale(self.id))
self._a_norm = Normalizer(self.world.env.get_action_size())
self._a_norm.set_mean_std(
-self.world.env.build_action_offset(self.id),
1 / self.world.env.build_action_scale(self.id))
return
def _build_bounds(self):
self._a_bound_min = self.world.env.build_action_bound_min(self.id)
self._a_bound_max = self.world.env.build_action_bound_max(self.id)
return
def _load_params(self, json_data):
if (self.ITERS_PER_UPDATE in json_data):
self.iters_per_update = int(json_data[self.ITERS_PER_UPDATE])
if (self.DISCOUNT_KEY in json_data):
self.discount = json_data[self.DISCOUNT_KEY]
if (self.MINI_BATCH_SIZE_KEY in json_data):
self.mini_batch_size = int(json_data[self.MINI_BATCH_SIZE_KEY])
if (self.REPLAY_BUFFER_SIZE_KEY in json_data):
self.replay_buffer_size = int(
json_data[self.REPLAY_BUFFER_SIZE_KEY])
if (self.INIT_SAMPLES_KEY in json_data):
self.init_samples = int(json_data[self.INIT_SAMPLES_KEY])
if (self.NORMALIZER_SAMPLES_KEY in json_data):
self.normalizer_samples = int(
json_data[self.NORMALIZER_SAMPLES_KEY])
if (self.OUTPUT_ITERS_KEY in json_data):
self.output_iters = json_data[self.OUTPUT_ITERS_KEY]
if (self.INT_OUTPUT_ITERS_KEY in json_data):
self.int_output_iters = json_data[self.INT_OUTPUT_ITERS_KEY]
if (self.TEST_EPISODES_KEY in json_data):
self.test_episodes = int(json_data[self.TEST_EPISODES_KEY])
if (self.EXP_ANNEAL_SAMPLES_KEY in json_data):
self.exp_anneal_samples = json_data[self.EXP_ANNEAL_SAMPLES_KEY]
if (self.EXP_PARAM_BEG_KEY in json_data):
self.exp_params_beg.load(json_data[self.EXP_PARAM_BEG_KEY])
if (self.EXP_PARAM_END_KEY in json_data):
self.exp_params_end.load(json_data[self.EXP_PARAM_END_KEY])
num_procs = MPIUtil.get_num_procs()
self.replay_buffer_size = int(
np.ceil(self.replay_buffer_size / num_procs))
self._local_mini_batch_size = int(
np.ceil(self.mini_batch_size / num_procs))
self._local_mini_batch_size = np.maximum(self._local_mini_batch_size,
1)
self.mini_batch_size = self._local_mini_batch_size * num_procs
assert (self.exp_params_beg.noise == self.exp_params_end.noise
) # noise std should not change
self.exp_params_curr = copy.deepcopy(self.exp_params_beg)
self.exp_params_end.noise = self.exp_params_beg.noise
self._need_normalizer_update = self.normalizer_samples > 0
return
def _record_state(self):
s = self.world.env.record_state(self.id)
return s
def _record_goal(self):
g = self.world.env.record_goal(self.id)
return g
def _record_reward(self):
r = self.world.env.calc_reward(self.id)
return r
def _apply_action(self, a):
self.world.env.set_action(self.id, a)
return
def _record_flags(self):
return int(0)
def _is_first_step(self):
return len(self.path.states) == 0
def _end_path(self):
s = self._record_state()
g = self._record_goal()
r = self._record_reward()
self.path.rewards.append(r)
self.path.states.append(s)
self.path.goals.append(g)
self.path.terminate = self.world.env.check_terminate(self.id)
return
def _update_new_action(self):
s = self._record_state()
g = self._record_goal()
if not (self._is_first_step()):
r = self._record_reward()
self.path.rewards.append(r)
a, logp = self._decide_action(s=s, g=g)
assert len(np.shape(a)) == 1
assert len(np.shape(logp)) <= 1
flags = self._record_flags()
self._apply_action(a)
self.path.states.append(s)
self.path.goals.append(g)
self.path.actions.append(a)
self.path.logps.append(logp)
self.path.flags.append(flags)
if self._enable_draw():
self._log_val(s, g)
return
def _update_exp_params(self):
lerp = float(self._total_sample_count) / self.exp_anneal_samples
lerp = np.clip(lerp, 0.0, 1.0)
self.exp_params_curr = self.exp_params_beg.lerp(
self.exp_params_end, lerp)
return
def _update_test_return(self, path):
path_reward = path.calc_return()
self.test_return += path_reward
self.test_episode_count += 1
if (self.test_episode_count * MPIUtil.get_num_procs() >=
self.test_episodes):
global_return = MPIUtil.reduce_sum(self.test_return)
global_count = MPIUtil.reduce_sum(self.test_episode_count)
avg_return = global_return / global_count
self.avg_test_return = avg_return
if self.enable_training:
self._init_mode_train()
return
def _init_mode_train(self):
self._mode = self.Mode.TRAIN
self.world.env.set_mode(self._mode)
return
def _init_mode_test(self):
self._mode = self.Mode.TEST
self.test_return = 0.0
self.test_episode_count = 0
self.world.env.set_mode(self._mode)
return
def _enable_output(self):
return MPIUtil.is_root_proc() and self.output_dir != ""
def _enable_int_output(self):
return MPIUtil.is_root_proc() and self.int_output_dir != ""
def _calc_val_bounds(self, discount):
r_min = self.world.env.get_reward_min(self.id)
r_max = self.world.env.get_reward_max(self.id)
assert (r_min <= r_max)
val_min = r_min / (1.0 - discount)
val_max = r_max / (1.0 - discount)
return val_min, val_max
def _calc_val_offset_scale(self, discount):
val_min, val_max = self._calc_val_bounds(discount)
val_offset = 0
val_scale = 1
if (np.isfinite(val_min) and np.isfinite(val_max)):
val_offset = -0.5 * (val_max + val_min)
val_scale = 2 / (val_max - val_min)
return val_offset, val_scale
def _calc_term_vals(self, discount):
r_fail = self.world.env.get_reward_fail(self.id)
r_succ = self.world.env.get_reward_succ(self.id)
r_min = self.world.env.get_reward_min(self.id)
r_max = self.world.env.get_reward_max(self.id)
assert (r_fail <= r_max and r_fail >= r_min)
assert (r_succ <= r_max and r_succ >= r_min)
assert (not np.isinf(r_fail))
assert (not np.isinf(r_succ))
if (discount == 0):
val_fail = 0
val_succ = 0
else:
val_fail = r_fail / (1.0 - discount)
val_succ = r_succ / (1.0 - discount)
return val_fail, val_succ
def _update_iter(self, iter):
if (self._enable_output() and self.iter % self.output_iters == 0):
output_path = self._get_output_path()
output_dir = os.path.dirname(output_path)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
self.save_model(output_path)
if (self._enable_int_output()
and self.iter % self.int_output_iters == 0):
int_output_path = self._get_int_output_path()
int_output_dir = os.path.dirname(int_output_path)
if not os.path.exists(int_output_dir):
os.makedirs(int_output_dir)
self.save_model(int_output_path)
self.iter = iter
return
def _enable_draw(self):
return self.world.env.enable_draw
def _log_val(self, s, g):
pass
def _build_replay_buffer(self, buffer_size):
num_procs = MPIUtil.get_num_procs()
buffer_size = int(buffer_size / num_procs)
self.replay_buffer = ReplayBuffer(buffer_size=buffer_size)
self.replay_buffer_initialized = False
return
def _store_path(self, path):
path_id = self.replay_buffer.store(path)
valid_path = path_id != MathUtil.INVALID_IDX
if valid_path:
self.train_return = path.calc_return()
if self._need_normalizer_update:
self._record_normalizers(path)
return path_id
def _record_normalizers(self, path):
states = np.array(path.states)
self._s_norm.record(states)
if self.has_goal():
goals = np.array(path.goals)
self._g_norm.record(goals)
return
def _update_normalizers(self):
self._s_norm.update()
if self.has_goal():
self._g_norm.update()
return
def _train(self):
samples = self.replay_buffer.total_count
self._total_sample_count = int(MPIUtil.reduce_sum(samples))
end_training = False
if (self.replay_buffer_initialized):
if (self._valid_train_step()):
prev_iter = self.iter
iters = self._get_iters_per_update()
avg_train_return = MPIUtil.reduce_avg(self.train_return)
for i in range(iters):
curr_iter = self.iter
wall_time = time.time() - self.start_time
wall_time /= 60 * 60 # store time in hours
has_goal = self.has_goal()
s_mean = np.mean(self._s_norm.mean)
s_std = np.mean(self._s_norm.std)
g_mean = np.mean(self._g_norm.mean) if has_goal else 0
g_std = np.mean(self._g_norm.std) if has_goal else 0
self.logger.log_tabular("Iteration", self.iter)
self.logger.log_tabular("Wall_Time", wall_time)
self.logger.log_tabular("Samples",
self._total_sample_count)
self.logger.log_tabular("Train_Return", avg_train_return)
self.logger.log_tabular("Test_Return",
self.avg_test_return)
self.logger.log_tabular("State_Mean", s_mean)
self.logger.log_tabular("State_Std", s_std)
self.logger.log_tabular("Goal_Mean", g_mean)
self.logger.log_tabular("Goal_Std", g_std)
self._log_exp_params()
self._update_iter(self.iter + 1)
self._train_step()
Logger.print("Agent " + str(self.id))
self.logger.print_tabular()
Logger.print("")
if (self._enable_output()
and curr_iter % self.int_output_iters == 0):
self.logger.dump_tabular()
if (prev_iter // self.int_output_iters !=
self.iter // self.int_output_iters):
end_training = self.enable_testing()
else:
Logger.print("Agent " + str(self.id))
Logger.print("Samples: " + str(self._total_sample_count))
Logger.print("")
if (self._total_sample_count >= self.init_samples):
self.replay_buffer_initialized = True
end_training = self.enable_testing()
if self._need_normalizer_update:
self._update_normalizers()
self._need_normalizer_update = self.normalizer_samples > self._total_sample_count
if end_training:
self._init_mode_test()
return
def _get_iters_per_update(self):
return MPIUtil.get_num_procs() * self.iters_per_update
def _valid_train_step(self):
exp_samples = self.replay_buffer.count_filtered(self.EXP_ACTION_FLAG)
return (exp_samples >= self._local_mini_batch_size)
def _log_exp_params(self):
self.logger.log_tabular("Exp_Rate", self.exp_params_curr.rate)
self.logger.log_tabular("Exp_Noise", self.exp_params_curr.noise)
return
class RLAgent(ABC):
class Mode(Enum):
TRAIN = 0
TEST = 1
TRAIN_END = 2
NAME = "None"
UPDATE_PERIOD_KEY = "UpdatePeriod"
ITERS_PER_UPDATE = "ItersPerUpdate"
DISCOUNT_KEY = "Discount"
MINI_BATCH_SIZE_KEY = "MiniBatchSize"
REPLAY_BUFFER_SIZE_KEY = "ReplayBufferSize"
INIT_SAMPLES_KEY = "InitSamples"
NORMALIZER_SAMPLES_KEY = "NormalizerSamples"
OUTPUT_ITERS_KEY = "OutputIters"
INT_OUTPUT_ITERS_KEY = "IntOutputIters"
TEST_EPISODES_KEY = "TestEpisodes"
EXP_ANNEAL_SAMPLES_KEY = "ExpAnnealSamples"
EXP_PARAM_BEG_KEY = "ExpParamsBeg"
EXP_PARAM_END_KEY = "ExpParamsEnd"
def __init__(self, world, id, json_data):
self.world = world
self.id = id
self.logger = Logger()
self._mode = self.Mode.TRAIN
assert self._check_action_space(), \
Logger.print2("Invalid action space, got {:s}".format(str(self.get_action_space())))
self._enable_training = True
self.path = Path()
self.iter = int(0)
self.start_time = time.time()
self._update_counter = 0
self.update_period = 1.0 # simulated time (seconds) before each training update
self.iters_per_update = int(1)
self.discount = 0.95
self.mini_batch_size = int(32)
self.replay_buffer_size = int(50000)
self.init_samples = int(1000)
self.normalizer_samples = np.inf
self._local_mini_batch_size = self.mini_batch_size # batch size for each work for multiprocessing
self._need_normalizer_update = True
self._total_sample_count = 0
self._output_dir = ""
self._int_output_dir = ""
self.output_iters = 100
self.int_output_iters = 100
self.train_return = 0.0
self.test_episodes = int(0)
self.test_episode_count = int(0)
self.test_return = 0.0
self.avg_test_return = 0.0
self.exp_anneal_samples = 320000
self.exp_params_beg = ExpParams()
self.exp_params_end = ExpParams()
self.exp_params_curr = ExpParams()
self._load_params(json_data)
self._build_replay_buffer(self.replay_buffer_size)
self._build_normalizers()
self._build_bounds()
self.reset()
return
def __str__(self):
action_space_str = str(self.get_action_space())
info_str = ""
info_str += '"ID": {:d},\n "Type": "{:s}",\n "ActionSpace": "{:s}",\n "StateDim": {:d},\n "GoalDim": {:d},\n "ActionDim": {:d}'.format(
self.id, self.NAME, action_space_str[action_space_str.rfind('.') + 1:], self.get_state_size(), self.get_goal_size(), self.get_action_size())
return "{\n" + info_str + "\n}"
def get_output_dir(self):
return self._output_dir
def set_output_dir(self, out_dir):
self._output_dir = out_dir
if (self._output_dir != ""):
self.logger.configure_output_file(out_dir + "/agent" + str(self.id) + "_log.txt")
return
output_dir = property(get_output_dir, set_output_dir)
def get_int_output_dir(self):
return self._int_output_dir
def set_int_output_dir(self, out_dir):
self._int_output_dir = out_dir
return
int_output_dir = property(get_int_output_dir, set_int_output_dir)
def reset(self):
self.path.clear()
return
def update(self, timestep):
if self.need_new_action():
#print("update_new_action!!!")
self._update_new_action()
else:
print("no action???")
if (self._mode == self.Mode.TRAIN and self.enable_training):
self._update_counter += timestep
while self._update_counter >= self.update_period:
self._train()
self._update_exp_params()
self.world.env.set_sample_count(self._total_sample_count)
self._update_counter -= self.update_period
return
def end_episode(self):
if (self.path.pathlength() > 0):
self._end_path()
if (self._mode == self.Mode.TRAIN or self._mode == self.Mode.TRAIN_END):
if (self.enable_training and self.path.pathlength() > 0):
self._store_path(self.path)
elif (self._mode == self.Mode.TEST):
self._update_test_return(self.path)
else:
assert False, Logger.print2("Unsupported RL agent mode" + str(self._mode))
self._update_mode()
return
def has_goal(self):
return self.get_goal_size() > 0
def predict_val(self):
return 0
def get_enable_training(self):
return self._enable_training
def set_enable_training(self, enable):
print("set_enable_training!=", enable)
self._enable_training = enable
if (self._enable_training):
self.reset()
return
enable_training = property(get_enable_training, set_enable_training)
def enable_testing(self):
return self.test_episodes > 0
def get_name(self):
return self.NAME
@abstractmethod
def save_model(self, out_path):
pass
@abstractmethod
def load_model(self, in_path):
pass
@abstractmethod
def _decide_action(self, s, g):
pass
@abstractmethod
def _get_output_path(self):
pass
@abstractmethod
def _get_int_output_path(self):
pass
@abstractmethod
def _train_step(self):
pass
@abstractmethod
def _check_action_space(self):
pass
def get_action_space(self):
return self.world.env.get_action_space(self.id)
def get_state_size(self):
return self.world.env.get_state_size(self.id)
def get_goal_size(self):
return self.world.env.get_goal_size(self.id)
def get_action_size(self):
return self.world.env.get_action_size(self.id)
def get_num_actions(self):
return self.world.env.get_num_actions(self.id)
def need_new_action(self):
return self.world.env.need_new_action(self.id)
def _build_normalizers(self):
self.s_norm = Normalizer(self.get_state_size(), self.world.env.build_state_norm_groups(self.id))
self.s_norm.set_mean_std(-self.world.env.build_state_offset(self.id),
1 / self.world.env.build_state_scale(self.id))
self.g_norm = Normalizer(self.get_goal_size(), self.world.env.build_goal_norm_groups(self.id))
self.g_norm.set_mean_std(-self.world.env.build_goal_offset(self.id),
1 / self.world.env.build_goal_scale(self.id))
self.a_norm = Normalizer(self.world.env.get_action_size())
self.a_norm.set_mean_std(-self.world.env.build_action_offset(self.id),
1 / self.world.env.build_action_scale(self.id))
return
def _build_bounds(self):
self.a_bound_min = self.world.env.build_action_bound_min(self.id)
self.a_bound_max = self.world.env.build_action_bound_max(self.id)
return
def _load_params(self, json_data):
if (self.UPDATE_PERIOD_KEY in json_data):
self.update_period = int(json_data[self.UPDATE_PERIOD_KEY])
if (self.ITERS_PER_UPDATE in json_data):
self.iters_per_update = int(json_data[self.ITERS_PER_UPDATE])
if (self.DISCOUNT_KEY in json_data):
self.discount = json_data[self.DISCOUNT_KEY]
if (self.MINI_BATCH_SIZE_KEY in json_data):
self.mini_batch_size = int(json_data[self.MINI_BATCH_SIZE_KEY])
if (self.REPLAY_BUFFER_SIZE_KEY in json_data):
self.replay_buffer_size = int(json_data[self.REPLAY_BUFFER_SIZE_KEY])
if (self.INIT_SAMPLES_KEY in json_data):
self.init_samples = int(json_data[self.INIT_SAMPLES_KEY])
if (self.NORMALIZER_SAMPLES_KEY in json_data):
self.normalizer_samples = int(json_data[self.NORMALIZER_SAMPLES_KEY])
if (self.OUTPUT_ITERS_KEY in json_data):
self.output_iters = json_data[self.OUTPUT_ITERS_KEY]
if (self.INT_OUTPUT_ITERS_KEY in json_data):
self.int_output_iters = json_data[self.INT_OUTPUT_ITERS_KEY]
if (self.TEST_EPISODES_KEY in json_data):
self.test_episodes = int(json_data[self.TEST_EPISODES_KEY])
if (self.EXP_ANNEAL_SAMPLES_KEY in json_data):
self.exp_anneal_samples = json_data[self.EXP_ANNEAL_SAMPLES_KEY]
if (self.EXP_PARAM_BEG_KEY in json_data):
self.exp_params_beg.load(json_data[self.EXP_PARAM_BEG_KEY])
if (self.EXP_PARAM_END_KEY in json_data):
self.exp_params_end.load(json_data[self.EXP_PARAM_END_KEY])
num_procs = MPIUtil.get_num_procs()
self._local_mini_batch_size = int(np.ceil(self.mini_batch_size / num_procs))
self._local_mini_batch_size = np.maximum(self._local_mini_batch_size, 1)
self.mini_batch_size = self._local_mini_batch_size * num_procs
assert(self.exp_params_beg.noise == self.exp_params_end.noise) # noise std should not change
self.exp_params_curr = copy.deepcopy(self.exp_params_beg)
self.exp_params_end.noise = self.exp_params_beg.noise
self._need_normalizer_update = self.normalizer_samples > 0
return
def _record_state(self):
s = self.world.env.record_state(self.id)
return s
def _record_goal(self):
g = self.world.env.record_goal(self.id)
return g
def _record_reward(self):
r = self.world.env.calc_reward(self.id)
return r
def _apply_action(self, a):
self.world.env.set_action(self.id, a)
return
def _record_flags(self):
return int(0)
def _is_first_step(self):
return len(self.path.states) == 0
def _end_path(self):
s = self._record_state()
g = self._record_goal()
r = self._record_reward()
self.path.rewards.append(r)
self.path.states.append(s)
self.path.goals.append(g)
self.path.terminate = self.world.env.check_terminate(self.id)
return
def _update_new_action(self):
s = self._record_state()
g = self._record_goal()
if not (self._is_first_step()):
r = self._record_reward()
self.path.rewards.append(r)
a, logp = self._decide_action(s=s, g=g)
assert len(np.shape(a)) == 1
assert len(np.shape(logp)) <= 1
flags = self._record_flags()
self._apply_action(a)
self.path.states.append(s)
self.path.goals.append(g)
self.path.actions.append(a)
self.path.logps.append(logp)
self.path.flags.append(flags)
if self._enable_draw():
self._log_val(s, g)
return
def _update_exp_params(self):
lerp = float(self._total_sample_count) / self.exp_anneal_samples
lerp = np.clip(lerp, 0.0, 1.0)
self.exp_params_curr = self.exp_params_beg.lerp(self.exp_params_end, lerp)
return
def _update_test_return(self, path):
path_reward = path.calc_return()
self.test_return += path_reward
self.test_episode_count += 1
return
def _update_mode(self):
if (self._mode == self.Mode.TRAIN):
self._update_mode_train()
elif (self._mode == self.Mode.TRAIN_END):
self._update_mode_train_end()
elif (self._mode == self.Mode.TEST):
self._update_mode_test()
else:
assert False, Logger.print2("Unsupported RL agent mode" + str(self._mode))
return
def _update_mode_train(self):
return
def _update_mode_train_end(self):
self._init_mode_test()
return
def _update_mode_test(self):
if (self.test_episode_count * MPIUtil.get_num_procs() >= self.test_episodes):
global_return = MPIUtil.reduce_sum(self.test_return)
global_count = MPIUtil.reduce_sum(self.test_episode_count)
avg_return = global_return / global_count
self.avg_test_return = avg_return
if self.enable_training:
self._init_mode_train()
return
def _init_mode_train(self):
self._mode = self.Mode.TRAIN
self.world.env.set_mode(self._mode)
return
def _init_mode_train_end(self):
self._mode = self.Mode.TRAIN_END
return
def _init_mode_test(self):
self._mode = self.Mode.TEST
self.test_return = 0.0
self.test_episode_count = 0
self.world.env.set_mode(self._mode)
return
def _enable_output(self):
return MPIUtil.is_root_proc() and self.output_dir != ""
def _enable_int_output(self):
return MPIUtil.is_root_proc() and self.int_output_dir != ""
def _calc_val_bounds(self, discount):
r_min = self.world.env.get_reward_min(self.id)
r_max = self.world.env.get_reward_max(self.id)
assert(r_min <= r_max)
val_min = r_min / ( 1.0 - discount)
val_max = r_max / ( 1.0 - discount)
return val_min, val_max
def _calc_val_offset_scale(self, discount):
val_min, val_max = self._calc_val_bounds(discount)
val_offset = 0
val_scale = 1
if (np.isfinite(val_min) and np.isfinite(val_max)):
val_offset = -0.5 * (val_max + val_min)
val_scale = 2 / (val_max - val_min)
return val_offset, val_scale
def _calc_term_vals(self, discount):
r_fail = self.world.env.get_reward_fail(self.id)
r_succ = self.world.env.get_reward_succ(self.id)
r_min = self.world.env.get_reward_min(self.id)
r_max = self.world.env.get_reward_max(self.id)
assert(r_fail <= r_max and r_fail >= r_min)
assert(r_succ <= r_max and r_succ >= r_min)
assert(not np.isinf(r_fail))
assert(not np.isinf(r_succ))
if (discount == 0):
val_fail = 0
val_succ = 0
else:
val_fail = r_fail / (1.0 - discount)
val_succ = r_succ / (1.0 - discount)
return val_fail, val_succ
def _update_iter(self, iter):
if (self._enable_output() and self.iter % self.output_iters == 0):
output_path = self._get_output_path()
output_dir = os.path.dirname(output_path)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
self.save_model(output_path)
if (self._enable_int_output() and self.iter % self.int_output_iters == 0):
int_output_path = self._get_int_output_path()
int_output_dir = os.path.dirname(int_output_path)
if not os.path.exists(int_output_dir):
os.makedirs(int_output_dir)
self.save_model(int_output_path)
self.iter = iter
return
def _enable_draw(self):
return self.world.env.enable_draw
def _log_val(self, s, g):
pass
def _build_replay_buffer(self, buffer_size):
num_procs = MPIUtil.get_num_procs()
buffer_size = int(buffer_size / num_procs)
self.replay_buffer = ReplayBuffer(buffer_size=buffer_size)
self.replay_buffer_initialized = False
return
def _store_path(self, path):
path_id = self.replay_buffer.store(path)
valid_path = path_id != MathUtil.INVALID_IDX
if valid_path:
self.train_return = path.calc_return()
if self._need_normalizer_update:
self._record_normalizers(path)
return path_id
def _record_normalizers(self, path):
states = np.array(path.states)
self.s_norm.record(states)
if self.has_goal():
goals = np.array(path.goals)
self.g_norm.record(goals)
return
def _update_normalizers(self):
self.s_norm.update()
if self.has_goal():
self.g_norm.update()
return
def _train(self):
samples = self.replay_buffer.total_count
self._total_sample_count = int(MPIUtil.reduce_sum(samples))
end_training = False
if (self.replay_buffer_initialized):
if (self._valid_train_step()):
prev_iter = self.iter
iters = self._get_iters_per_update()
avg_train_return = MPIUtil.reduce_avg(self.train_return)
for i in range(iters):
curr_iter = self.iter
wall_time = time.time() - self.start_time
wall_time /= 60 * 60 # store time in hours
has_goal = self.has_goal()
s_mean = np.mean(self.s_norm.mean)
s_std = np.mean(self.s_norm.std)
g_mean = np.mean(self.g_norm.mean) if has_goal else 0
g_std = np.mean(self.g_norm.std) if has_goal else 0
self.logger.log_tabular("Iteration", self.iter)
self.logger.log_tabular("Wall_Time", wall_time)
self.logger.log_tabular("Samples", self._total_sample_count)
self.logger.log_tabular("Train_Return", avg_train_return)
self.logger.log_tabular("Test_Return", self.avg_test_return)
self.logger.log_tabular("State_Mean", s_mean)
self.logger.log_tabular("State_Std", s_std)
self.logger.log_tabular("Goal_Mean", g_mean)
self.logger.log_tabular("Goal_Std", g_std)
self._log_exp_params()
self._update_iter(self.iter + 1)
self._train_step()
Logger.print2("Agent " + str(self.id))
self.logger.print_tabular()
Logger.print2("")
if (self._enable_output() and curr_iter % self.int_output_iters == 0):
self.logger.dump_tabular()
if (prev_iter // self.int_output_iters != self.iter // self.int_output_iters):
end_training = self.enable_testing()
else:
Logger.print2("Agent " + str(self.id))
Logger.print2("Samples: " + str(self._total_sample_count))
Logger.print2("")
if (self._total_sample_count >= self.init_samples):
self.replay_buffer_initialized = True
end_training = self.enable_testing()
if self._need_normalizer_update:
self._update_normalizers()
self._need_normalizer_update = self.normalizer_samples > self._total_sample_count
if end_training:
self._init_mode_train_end()
return
def _get_iters_per_update(self):
return MPIUtil.get_num_procs() * self.iters_per_update
def _valid_train_step(self):
return True
def _log_exp_params(self):
self.logger.log_tabular("Exp_Rate", self.exp_params_curr.rate)
self.logger.log_tabular("Exp_Noise", self.exp_params_curr.noise)
self.logger.log_tabular("Exp_Temp", self.exp_params_curr.temp)
return
