def update(self):
new_count = MPIUtil.reduce_sum(self.new_count)
new_sum = MPIUtil.reduce_sum(self.new_sum)
new_sum_sq = MPIUtil.reduce_sum(self.new_sum_sq)
new_total = self.count + new_count
if (self.count // self.CHECK_SYNC_COUNT != new_total // self.CHECK_SYNC_COUNT):
assert self.check_synced(), Logger.print2('Normalizer parameters desynchronized')
if new_count > 0:
new_mean = self._process_group_data(new_sum / new_count, self.mean)
new_mean_sq = self._process_group_data(new_sum_sq / new_count, self.mean_sq)
w_old = float(self.count) / new_total
w_new = float(new_count) / new_total
self.mean = w_old * self.mean + w_new * new_mean
self.mean_sq = w_old * self.mean_sq + w_new * new_mean_sq
self.count = new_total
self.std = self.calc_std(self.mean, self.mean_sq)
self.new_count = 0
self.new_sum.fill(0)
self.new_sum_sq.fill(0)
return
def update(self):
new_count = MPIUtil.reduce_sum(self.new_count)
new_sum = MPIUtil.reduce_sum(self.new_sum)
new_sum_sq = MPIUtil.reduce_sum(self.new_sum_sq)
new_total = self.count + new_count
if (self.count // self.CHECK_SYNC_COUNT !=
new_total // self.CHECK_SYNC_COUNT):
assert self.check_synced(), Logger.print2(
'Normalizer parameters desynchronized')
if new_count > 0:
new_mean = self._process_group_data(new_sum / new_count, self.mean)
new_mean_sq = self._process_group_data(new_sum_sq / new_count,
self.mean_sq)
w_old = float(self.count) / new_total
w_new = float(new_count) / new_total
self.mean = w_old * self.mean + w_new * new_mean
self.mean_sq = w_old * self.mean_sq + w_new * new_mean_sq
self.count = new_total
self.std = self.calc_std(self.mean, self.mean_sq)
self.new_count = 0
self.new_sum.fill(0)
self.new_sum_sq.fill(0)
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 _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 _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 _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 _valid_train_step(self):
samples = self.replay_buffer.get_current_size()
exp_samples = self.replay_buffer.count_filtered(self.EXP_ACTION_FLAG)
global_sample_count = int(MPIUtil.reduce_sum(samples))
global_exp_min = int(MPIUtil.reduce_min(exp_samples))
return (global_sample_count > self.batch_size) and (global_exp_min > 0)
def _train_step(self):
adv_eps = 1e-5
start_idx = self.replay_buffer.buffer_tail
end_idx = self.replay_buffer.buffer_head
assert (start_idx == 0)
assert (self.replay_buffer.get_current_size() <=
self.replay_buffer.buffer_size) # must avoid overflow
assert (start_idx < end_idx)
idx = np.array(list(range(start_idx, end_idx)))
end_mask = self.replay_buffer.is_path_end(idx)
end_mask = np.logical_not(end_mask)
vals = self._compute_batch_vals(start_idx, end_idx)
new_vals = self._compute_batch_new_vals(start_idx, end_idx, vals)
valid_idx = idx[end_mask]
exp_idx = self.replay_buffer.get_idx_filtered(
self.EXP_ACTION_FLAG).copy()
num_valid_idx = valid_idx.shape[0]
num_exp_idx = exp_idx.shape[0]
exp_idx = np.column_stack(
[exp_idx,
np.array(list(range(0, num_exp_idx)), dtype=np.int32)])
local_sample_count = valid_idx.size
global_sample_count = int(MPIUtil.reduce_sum(local_sample_count))
mini_batches = int(np.ceil(global_sample_count / self.mini_batch_size))
adv = new_vals[exp_idx[:, 0]] - vals[exp_idx[:, 0]]
new_vals = np.clip(new_vals, self.val_min, self.val_max)
adv_mean = np.mean(adv)
adv_std = np.std(adv)
adv = (adv - adv_mean) / (adv_std + adv_eps)
adv = np.clip(adv, -self.norm_adv_clip, self.norm_adv_clip)
critic_loss = 0
actor_loss = 0
actor_clip_frac = 0
for e in range(self.epochs):
np.random.shuffle(valid_idx)
np.random.shuffle(exp_idx)
for b in range(mini_batches):
batch_idx_beg = b * self._local_mini_batch_size
batch_idx_end = batch_idx_beg + self._local_mini_batch_size
critic_batch = np.array(range(batch_idx_beg, batch_idx_end),
dtype=np.int32)
actor_batch = critic_batch.copy()
critic_batch = np.mod(critic_batch, num_valid_idx)
actor_batch = np.mod(actor_batch, num_exp_idx)
shuffle_actor = (actor_batch[-1] < actor_batch[0]) or (
actor_batch[-1] == num_exp_idx - 1)
critic_batch = valid_idx[critic_batch]
actor_batch = exp_idx[actor_batch]
critic_batch_vals = new_vals[critic_batch]
actor_batch_adv = adv[actor_batch[:, 1]]
critic_s = self.replay_buffer.get('states', critic_batch)
critic_g = self.replay_buffer.get(
'goals', critic_batch) if self.has_goal() else None
curr_critic_loss = self._update_critic(critic_s, critic_g,
critic_batch_vals)
actor_s = self.replay_buffer.get("states", actor_batch[:, 0])
actor_g = self.replay_buffer.get(
"goals", actor_batch[:, 0]) if self.has_goal() else None
actor_a = self.replay_buffer.get("actions", actor_batch[:, 0])
actor_logp = self.replay_buffer.get("logps", actor_batch[:, 0])
curr_actor_loss, curr_actor_clip_frac = self._update_actor(
actor_s, actor_g, actor_a, actor_logp, actor_batch_adv)
critic_loss += curr_critic_loss
actor_loss += np.abs(curr_actor_loss)
actor_clip_frac += curr_actor_clip_frac
if (shuffle_actor):
np.random.shuffle(exp_idx)
total_batches = mini_batches * self.epochs
critic_loss /= total_batches
actor_loss /= total_batches
actor_clip_frac /= total_batches
critic_loss = MPIUtil.reduce_avg(critic_loss)
actor_loss = MPIUtil.reduce_avg(actor_loss)
actor_clip_frac = MPIUtil.reduce_avg(actor_clip_frac)
critic_stepsize = self.critic_solver.get_stepsize()
actor_stepsize = self.update_actor_stepsize(actor_clip_frac)
self.logger.log_tabular('Critic_Loss', critic_loss)
self.logger.log_tabular('Critic_Stepsize', critic_stepsize)
self.logger.log_tabular('Actor_Loss', actor_loss)
self.logger.log_tabular('Actor_Stepsize', actor_stepsize)
self.logger.log_tabular('Clip_Frac', actor_clip_frac)
self.logger.log_tabular('Adv_Mean', adv_mean)
self.logger.log_tabular('Adv_Std', adv_std)
self.replay_buffer.clear()
return
def _valid_train_step(self):
samples = self.replay_buffer.get_current_size()
exp_samples = self.replay_buffer.count_filtered(self.EXP_ACTION_FLAG)
global_sample_count = int(MPIUtil.reduce_sum(samples))
global_exp_min = int(MPIUtil.reduce_min(exp_samples))
return (global_sample_count > self.batch_size) and (global_exp_min > 0)
def _train_step(self):
adv_eps = 1e-5
start_idx = self.replay_buffer.buffer_tail
end_idx = self.replay_buffer.buffer_head
assert(start_idx == 0)
assert(self.replay_buffer.get_current_size() <= self.replay_buffer.buffer_size) # must avoid overflow
assert(start_idx < end_idx)
idx = np.array(list(range(start_idx, end_idx)))
end_mask = self.replay_buffer.is_path_end(idx)
end_mask = np.logical_not(end_mask)
vals = self._compute_batch_vals(start_idx, end_idx)
new_vals = self._compute_batch_new_vals(start_idx, end_idx, vals)
valid_idx = idx[end_mask]
exp_idx = self.replay_buffer.get_idx_filtered(self.EXP_ACTION_FLAG).copy()
num_valid_idx = valid_idx.shape[0]
num_exp_idx = exp_idx.shape[0]
exp_idx = np.column_stack([exp_idx, np.array(list(range(0, num_exp_idx)), dtype=np.int32)])
local_sample_count = valid_idx.size
global_sample_count = int(MPIUtil.reduce_sum(local_sample_count))
mini_batches = int(np.ceil(global_sample_count / self.mini_batch_size))
adv = new_vals[exp_idx[:,0]] - vals[exp_idx[:,0]]
new_vals = np.clip(new_vals, self.val_min, self.val_max)
adv_mean = np.mean(adv)
adv_std = np.std(adv)
adv = (adv - adv_mean) / (adv_std + adv_eps)
adv = np.clip(adv, -self.norm_adv_clip, self.norm_adv_clip)
critic_loss = 0
actor_loss = 0
actor_clip_frac = 0
for e in range(self.epochs):
np.random.shuffle(valid_idx)
np.random.shuffle(exp_idx)
for b in range(mini_batches):
batch_idx_beg = b * self._local_mini_batch_size
batch_idx_end = batch_idx_beg + self._local_mini_batch_size
critic_batch = np.array(range(batch_idx_beg, batch_idx_end), dtype=np.int32)
actor_batch = critic_batch.copy()
critic_batch = np.mod(critic_batch, num_valid_idx)
actor_batch = np.mod(actor_batch, num_exp_idx)
shuffle_actor = (actor_batch[-1] < actor_batch[0]) or (actor_batch[-1] == num_exp_idx - 1)
critic_batch = valid_idx[critic_batch]
actor_batch = exp_idx[actor_batch]
critic_batch_vals = new_vals[critic_batch]
actor_batch_adv = adv[actor_batch[:,1]]
critic_s = self.replay_buffer.get('states', critic_batch)
critic_g = self.replay_buffer.get('goals', critic_batch) if self.has_goal() else None
curr_critic_loss = self._update_critic(critic_s, critic_g, critic_batch_vals)
actor_s = self.replay_buffer.get("states", actor_batch[:,0])
actor_g = self.replay_buffer.get("goals", actor_batch[:,0]) if self.has_goal() else None
actor_a = self.replay_buffer.get("actions", actor_batch[:,0])
actor_logp = self.replay_buffer.get("logps", actor_batch[:,0])
curr_actor_loss, curr_actor_clip_frac = self._update_actor(actor_s, actor_g, actor_a, actor_logp, actor_batch_adv)
critic_loss += curr_critic_loss
actor_loss += np.abs(curr_actor_loss)
actor_clip_frac += curr_actor_clip_frac
if (shuffle_actor):
np.random.shuffle(exp_idx)
total_batches = mini_batches * self.epochs
critic_loss /= total_batches
actor_loss /= total_batches
actor_clip_frac /= total_batches
critic_loss = MPIUtil.reduce_avg(critic_loss)
actor_loss = MPIUtil.reduce_avg(actor_loss)
actor_clip_frac = MPIUtil.reduce_avg(actor_clip_frac)
critic_stepsize = self.critic_solver.get_stepsize()
actor_stepsize = self.update_actor_stepsize(actor_clip_frac)
self.logger.log_tabular('Critic_Loss', critic_loss)
self.logger.log_tabular('Critic_Stepsize', critic_stepsize)
self.logger.log_tabular('Actor_Loss', actor_loss)
self.logger.log_tabular('Actor_Stepsize', actor_stepsize)
self.logger.log_tabular('Clip_Frac', actor_clip_frac)
self.logger.log_tabular('Adv_Mean', adv_mean)
self.logger.log_tabular('Adv_Std', adv_std)
self.replay_buffer.clear()
return