def train(variant):
s_save = []
env_name = variant['env_name']
env = get_env_from_name(env_name)
if variant['evaluate'] is True:
evaluation_env = get_env_from_name(env_name)
else:
evaluation_env = None
env_params = variant['env_params']
judge_safety_func = get_safety_constraint_func(variant)
max_episodes = env_params['max_episodes']
max_ep_steps = env_params['max_ep_steps']
max_global_steps = env_params['max_global_steps']
store_last_n_paths = variant['store_last_n_paths']
evaluation_frequency = variant['evaluation_frequency']
num_of_paths = variant['num_of_paths']
alg_name = variant['algorithm_name']
policy_build_fn = get_policy(alg_name)
policy_params = variant['alg_params']
min_memory_size = policy_params['min_memory_size']
steps_per_cycle = policy_params['steps_per_cycle']
train_per_cycle = policy_params['train_per_cycle']
lr_a, lr_c, lr_l = policy_params['lr_a'], policy_params[
'lr_c'], policy_params['lr_l']
lr_a_now = lr_a # learning rate for actor
lr_c_now = lr_c # learning rate for critic
lr_l_now = lr_l # learning rate for critic
log_path = variant['log_path']
logger.configure(dir=log_path, format_strs=['csv'])
logger.logkv('tau', policy_params['tau'])
logger.logkv('alpha3', policy_params['alpha3'])
logger.logkv('batch_size', policy_params['batch_size'])
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.shape[0]
a_upperbound = env.action_space.high
a_lowerbound = env.action_space.low
policy = policy_build_fn(a_dim, s_dim, policy_params)
logger.logkv('target_entropy', policy.target_entropy)
# For analyse
Render = env_params['eval_render']
ewma_p = 0.95
ewma_step = np.zeros((1, max_episodes + 1))
ewma_reward = np.zeros((1, max_episodes + 1))
# Training setting
t1 = time.time()
global_step = 0
last_training_paths = deque(maxlen=store_last_n_paths)
training_started = False
for i in range(max_episodes):
ep_reward = 0
l_r = 0
current_path = {
'rewards': [],
'l_rewards': [],
'violation': [],
}
[current_path.update({key: []}) for key in policy.diag_names]
if global_step > max_global_steps:
break
s = env.reset()
for j in range(max_ep_steps):
if Render:
env.render()
a = policy.choose_action(s)
action = a_lowerbound + (a + 1.) * (a_upperbound -
a_lowerbound) / 2
# Run in simulator
s_, r, done, info = env.step(action)
if training_started:
global_step += 1
l_r = info['l_rewards']
if j == max_ep_steps - 1:
done = True
terminal = 1. if done else 0.
violation_of_constraint = info['violation_of_constraint']
# 储存s,a和s_next,reward用于DDPG的学习
policy.store_transition(s, a, r, l_r, terminal, s_)
s_save.append(s_)
sio.savemat('data_all.mat', {
's': s_save,
})
# 如果状态接近边缘 就存储到边缘memory里
# if policy.use_lyapunov is True and np.abs(s[0]) > env.cons_pos: # or np.abs(s[2]) > env.theta_threshold_radians*0.8
if policy.use_lyapunov is True and judge_safety_func(
s_, r, done,
info): # or np.abs(s[2]) > env.theta_threshold_radians*0.8
policy.store_edge_transition(s, a, r, l_r, terminal, s_)
# Learn
if policy.use_lyapunov is True:
if policy.pointer > min_memory_size and global_step % steps_per_cycle == 0:
# Decay the action randomness
training_started = True
for _ in range(train_per_cycle):
train_diagnotic = policy.learn(lr_a_now, lr_c_now,
lr_l_now)
else:
if policy.pointer > min_memory_size and global_step % steps_per_cycle == 0:
# Decay the action randomness
training_started = True
for _ in range(train_per_cycle):
train_diagnotic = policy.learn(lr_a_now, lr_c_now,
lr_l_now)
if training_started:
current_path['rewards'].append(r)
current_path['l_rewards'].append(l_r)
current_path['violation'].append(violation_of_constraint)
[
current_path[key].append(value)
for key, value in zip(policy.diag_names, train_diagnotic)
]
if training_started and global_step % evaluation_frequency == 0 and global_step > 0:
if evaluation_env is not None:
rollouts = get_evaluation_rollouts(policy,
evaluation_env,
num_of_paths,
max_ep_steps,
render=Render)
diagnotic = evaluate_rollouts(rollouts)
# [diagnotics[key].append(diagnotic[key]) for key in diagnotic.keys()]
print(
'training_step:',
global_step,
'average eval reward:',
diagnotic['return-average'],
'average eval lreward:',
diagnotic['lreturn-average'],
'average eval violations:',
diagnotic['violation-avg'],
'average length:',
diagnotic['episode-length-avg'],
)
logger.logkv('eval_eprewmean', diagnotic['return-average'])
logger.logkv('eval_eplrewmean',
diagnotic['lreturn-average'])
logger.logkv('eval_eplenmean',
diagnotic['episode-length-avg'])
logger.logkv('eval_violation_times',
diagnotic['violation-avg'])
logger.logkv("total_timesteps", global_step)
training_diagnotic = evaluate_training_rollouts(
last_training_paths)
if training_diagnotic is not None:
# [training_diagnotics[key].append(training_diagnotic[key]) for key in training_diagnotic.keys()]\
logger.logkv('eprewmean', training_diagnotic['rewards'])
logger.logkv('eplrewmean', training_diagnotic['l_rewards'])
logger.logkv('eplenmean', training_diagnotic['len'])
logger.logkv('end_cost', training_diagnotic['end_cost'])
[
logger.logkv(key, training_diagnotic[key])
for key in policy.diag_names
]
logger.logkv('violation_times',
training_diagnotic['violation'])
logger.logkv('lr_a', lr_a_now)
logger.logkv('lr_c', lr_c_now)
logger.logkv('lr_l', lr_l_now)
print(
'training_step:',
global_step,
'average reward:',
round(training_diagnotic['rewards'], 2),
'average lreward:',
round(training_diagnotic['l_rewards'], 2),
'average violations:',
training_diagnotic['violation'],
'end cost:',
round(training_diagnotic['end_cost'], 2),
'average length:',
round(training_diagnotic['len'], 1),
'lyapunov error:',
round(training_diagnotic['lyapunov_error'], 6),
'critic1 error:',
round(training_diagnotic['critic1_error'], 6),
'critic2 error:',
round(training_diagnotic['critic2_error'], 6),
'policy_loss:',
round(training_diagnotic['policy_loss'], 6),
'alpha:',
round(training_diagnotic['alpha'], 6),
'lambda:',
round(training_diagnotic['labda'], 6),
'entropy:',
round(training_diagnotic['entropy'], 6),
)
# 'max_grad:', round(training_diagnotic['max_grad'], 6)
logger.dumpkvs()
# 状态更新
s = s_
ep_reward += r
# OUTPUT TRAINING INFORMATION AND LEARNING RATE DECAY
if done:
if training_started:
last_training_paths.appendleft(current_path)
ewma_step[0,
i + 1] = ewma_p * ewma_step[0, i] + (1 - ewma_p) * j
ewma_reward[
0, i +
1] = ewma_p * ewma_reward[0, i] + (1 - ewma_p) * ep_reward
frac = 1.0 - (global_step - 1.0) / max_global_steps
lr_a_now = lr_a * frac # learning rate for actor
lr_c_now = lr_c * frac # learning rate for critic
lr_l_now = lr_l * frac # learning rate for critic
break
policy.save_result(log_path)
print('Running time: ', time.time() - t1)
return
def train(variant):
env_name = variant['env_name']
env = get_env_from_name(env_name)
if variant['evaluate'] is True:
evaluation_env = get_env_from_name(env_name)
else:
evaluation_env = None
env_params = variant['env_params']
judge_safety_func = get_safety_constraint_func(variant)
max_episodes = env_params['max_episodes']
max_ep_steps = env_params['max_ep_steps']
max_global_steps = env_params['max_global_steps']
store_last_n_paths = variant['store_last_n_paths']
evaluation_frequency = variant['evaluation_frequency']
num_of_paths = variant['num_of_paths']
alg_name = variant['algorithm_name']
policy_build_fn = get_policy(alg_name)
policy_params = variant['alg_params']
min_memory_size = policy_params['min_memory_size']
steps_per_cycle = policy_params['steps_per_cycle']
train_per_cycle = policy_params['train_per_cycle']
lr_a, lr_c, lr_l = policy_params['lr_a'], policy_params[
'lr_c'], policy_params['lr_l']
lr_a_now = lr_a # learning rate for actor
lr_c_now = lr_c # learning rate for critic
lr_l_now = lr_l # learning rate for critic
log_path = variant['log_path']
logger.configure(dir=log_path, format_strs=['csv'])
logger.logkv('tau', policy_params['tau'])
logger.logkv('alpha3', policy_params['alpha3'])
logger.logkv('batch_size', policy_params['batch_size'])
if 'Fetch' in env_name or 'Hand' in env_name:
s_dim = env.observation_space.spaces['observation'].shape[0]\
+ env.observation_space.spaces['achieved_goal'].shape[0]+ \
env.observation_space.spaces['desired_goal'].shape[0]
else:
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.shape[0]
a_upperbound = env.action_space.high
a_lowerbound = env.action_space.low
policy = policy_build_fn(a_dim, s_dim, policy_params)
logger.logkv('target_entropy', policy.target_entropy)
# For analyse
Render = env_params['eval_render']
ewma_p = 0.95
ewma_step = np.zeros((1, max_episodes + 1))
ewma_reward = np.zeros((1, max_episodes + 1))
# Training setting
t1 = time.time()
global_step = 0
last_training_paths = deque(maxlen=store_last_n_paths)
training_started = False
for i in range(max_episodes):
ep_reward = 0
l_r = 0
current_path = {
'rewards': [],
'l_rewards': [],
'l_error': [],
'critic1_error': [],
'critic2_error': [],
'alpha': [],
'lambda': [],
'entropy': [],
'a_loss': [],
'violation': [],
}
if global_step > max_global_steps:
break
s = env.reset()
if 'Fetch' in env_name or 'Hand' in env_name:
s = np.concatenate([s[key] for key in s.keys()])
for j in range(max_ep_steps):
if Render:
env.render()
a = policy.choose_action(s, True)
action = a_lowerbound + (a + 1.) * (a_upperbound -
a_lowerbound) / 2
# Run in simulator
s_, r, done, info = env.step(action)
if 'Fetch' in env_name or 'Hand' in env_name:
s_ = np.concatenate([s_[key] for key in s_.keys()])
if info['done'] > 0:
done = True
if training_started:
global_step += 1
l_r = info['l_rewards']
if j == max_ep_steps - 1:
done = True
terminal = 1. if done else 0.
violation_of_constraint = info['violation_of_constraint']
# 储存s,a和s_next,reward用于DDPG的学习
policy.store_transition(s, a, r, l_r, terminal, s_)
# Learn
if policy.pointer > min_memory_size and global_step % steps_per_cycle == 0:
# Decay the action randomness
training_started = True
for _ in range(train_per_cycle):
labda, alpha, c1_loss, c2_loss, l_loss, entropy, a_loss = policy.learn(
lr_a_now, lr_c_now, lr_l_now)
if training_started:
current_path['rewards'].append(r)
current_path['l_rewards'].append(l_r)
current_path['l_error'].append(l_loss)
current_path['critic1_error'].append(c1_loss)
current_path['critic2_error'].append(c2_loss)
current_path['alpha'].append(alpha)
current_path['lambda'].append(labda)
current_path['entropy'].append(entropy)
current_path['a_loss'].append(a_loss)
current_path['violation'].append(violation_of_constraint)
if training_started and global_step % evaluation_frequency == 0 and global_step > 0:
if evaluation_env is not None:
rollouts = get_evaluation_rollouts(policy,
evaluation_env,
num_of_paths,
max_ep_steps,
render=Render)
diagnotic = evaluate_rollouts(rollouts)
# [diagnotics[key].append(diagnotic[key]) for key in diagnotic.keys()]
print(
'training_step:',
global_step,
'average eval reward:',
diagnotic['return-average'],
'average eval lreward:',
diagnotic['lreturn-average'],
'average eval violations:',
diagnotic['violation-avg'],
'average length:',
diagnotic['episode-length-avg'],
)
logger.logkv('eval_eprewmean', diagnotic['return-average'])
logger.logkv('eval_eplrewmean',
diagnotic['lreturn-average'])
logger.logkv('eval_eplenmean',
diagnotic['episode-length-avg'])
logger.logkv('eval_violation_times',
diagnotic['violation-avg'])
logger.logkv("total_timesteps", global_step)
training_diagnotic = evaluate_training_rollouts(
last_training_paths)
if training_diagnotic is not None:
# [training_diagnotics[key].append(training_diagnotic[key]) for key in training_diagnotic.keys()]\
logger.logkv('eprewmean',
training_diagnotic['train-return-average'])
logger.logkv('eplrewmean',
training_diagnotic['train-lreturn-average'])
logger.logkv(
'eplenmean',
training_diagnotic['train-episode-length-avg'])
logger.logkv('lyapunov_lambda',
training_diagnotic['train-lambda-avg'])
logger.logkv('alpha',
training_diagnotic['train-alpha-avg'])
logger.logkv('entropy',
training_diagnotic['train-entropy-avg'])
logger.logkv('critic1 error',
training_diagnotic['train-critic1-error-avg'])
logger.logkv('critic2 error',
training_diagnotic['train-critic2-error-avg'])
logger.logkv(
'lyapunov error',
training_diagnotic['train-lyapunov-error-avg'])
logger.logkv('policy_loss',
training_diagnotic['train-a-loss-avg'])
logger.logkv(
'average_cost',
training_diagnotic['train-return-average'] /
training_diagnotic['train-episode-length-avg'])
logger.logkv('lr_a', lr_a_now)
logger.logkv('lr_c', lr_c_now)
logger.logkv('lr_l', lr_l_now)
print(
'training_step:',
global_step,
'average reward:',
round(training_diagnotic['train-return-average'], 2),
'average lreward:',
round(training_diagnotic['train-lreturn-average'], 2),
'average violations:',
training_diagnotic['train-violation-avg'],
'average length:',
round(training_diagnotic['train-episode-length-avg'],
1),
'lyapunov error:',
round(training_diagnotic['train-lyapunov-error-avg'],
6),
'critic1 error:',
round(training_diagnotic['train-critic1-error-avg'],
6),
'critic2 error:',
round(training_diagnotic['train-critic2-error-avg'],
6),
'policy_loss:',
round(training_diagnotic['train-a-loss-avg'], 6),
'alpha:',
round(training_diagnotic['train-alpha-avg'], 6),
'lambda:',
round(training_diagnotic['train-lambda-avg'], 6),
'entropy:',
round(training_diagnotic['train-entropy-avg'], 6),
)
logger.dumpkvs()
# 状态更新
s = s_
ep_reward += r
# OUTPUT TRAINING INFORMATION AND LEARNING RATE DECAY
if done:
if training_started:
last_training_paths.appendleft(current_path)
ewma_step[0,
i + 1] = ewma_p * ewma_step[0, i] + (1 - ewma_p) * j
ewma_reward[
0, i +
1] = ewma_p * ewma_reward[0, i] + (1 - ewma_p) * ep_reward
frac = 1.0 - (global_step - 1.0) / max_global_steps
lr_a_now = lr_a * frac # learning rate for actor
lr_c_now = lr_c * frac # learning rate for critic
lr_l_now = lr_l * frac # learning rate for critic
break
policy.save_result(log_path)
print('Running time: ', time.time() - t1)
return
def eval(variant):
env_name = variant['env_name']
env = get_env_from_name(env_name)
env_params = variant['env_params']
max_episodes = env_params['max_episodes']
max_ep_steps = env_params['max_ep_steps']
alg_name = variant['algorithm_name']
policy_build_fn = get_policy(alg_name)
policy_params = variant['alg_params']
root_path = variant['log_path']
if 'Fetch' in env_name or 'Hand' in env_name:
s_dim = env.observation_space.spaces['observation'].shape[0]\
+ env.observation_space.spaces['achieved_goal'].shape[0]+ \
env.observation_space.spaces['desired_goal'].shape[0]
else:
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.shape[0]
a_upperbound = env.action_space.high
a_lowerbound = env.action_space.low
policy = policy_build_fn(a_dim, s_dim, policy_params)
if 'CartPole' in env_name:
mag = env_params['impulse_mag']
# For analyse
Render = env_params['eval_render']
# Training setting
t1 = time.time()
die_count = 0
for i in range(variant['num_of_trials']):
log_path = variant['log_path'] + '/eval/' + str(0)
policy.restore(variant['log_path'] + '/' + str(0))
logger.configure(dir=log_path, format_strs=['csv'])
s = env.reset()
if 'Fetch' in env_name or 'Hand' in env_name:
s = np.concatenate([s[key] for key in s.keys()])
for j in range(max_ep_steps):
if Render:
env.render()
a = policy.choose_action(s, True)
action = a_lowerbound + (a + 1.) * (a_upperbound -
a_lowerbound) / 2
if j == 100 and 'CartPole' in env_name:
impulse = mag * np.sign(s[0])
# print('impulse comming:',impulse)
# Run in simulator
s_, r, done, info = env.step(action, impulse=impulse)
else:
s_, r, done, info = env.step(action)
if 'Fetch' in env_name or 'Hand' in env_name:
s_ = np.concatenate([s_[key] for key in s_.keys()])
if info['done'] > 0:
done = True
logger.logkv('rewards', r)
logger.logkv('timestep', j)
logger.dumpkvs()
l_r = info['l_rewards']
if j == max_ep_steps - 1:
done = True
s = s_
if done:
if j < 200:
die_count += 1
print('episode:', i, 'death:', die_count, 'mag:', mag)
break
print('Running time: ', time.time() - t1)
return
def train(variant):
env_name = variant['env_name']
env = get_env_from_name(env_name)
evaluation_env = get_env_from_name(env_name)
env_params = variant['env_params']
max_episodes = env_params['max_episodes']
max_ep_steps = env_params['max_ep_steps']
max_global_steps = env_params['max_global_steps']
store_last_n_paths = variant['store_last_n_paths']
evaluation_frequency = variant['evaluation_frequency']
alg_name = variant['algorithm_name']
policy_build_fn = get_policy(alg_name)
policy_params = variant['alg_params']
batch_size = policy_params['batch_size']
lr_c = policy_params['lr_c']
cliprange = policy_params['cliprange']
cliprangenow = cliprange
lr_c_now = lr_c # learning rate for critic
gamma = policy_params['gamma']
gae_lamda = policy_params['gae_lamda']
log_path = variant['log_path']
logger.configure(dir=log_path, format_strs=policy_params['output_format'])
logger.logkv('safety_threshold', policy_params['safety_threshold'])
logger.logkv('alpha3', policy_params['alpha3'])
logger.logkv('batch_size', batch_size)
if 'Fetch' in env_name or 'Hand' in env_name:
s_dim = env.observation_space.spaces['observation'].shape[0]\
+ env.observation_space.spaces['achieved_goal'].shape[0]+ \
env.observation_space.spaces['desired_goal'].shape[0]
else:
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.shape[0]
a_upperbound = env.action_space.high
a_lowerbound = env.action_space.low
policy = policy_build_fn(a_dim, s_dim, policy_params)
# For analyse
Render = env_params['eval_render']
# Training setting
t1 = time.time()
global_step = 0
last_training_paths = deque(maxlen=policy.N)
for j in range(max_global_steps):
if global_step > max_global_steps:
break
mb_obs, mb_obs_, mb_rewards, mb_actions, mb_values, mb_terminals, mb_t = [], [], [], [], [], [], []
for n in range(policy.N):
current_path = {
'rewards': [],
'obs': [],
'obs_': [],
'done': [],
'value': [],
't': [],
'action': [],
}
s = env.reset()
if 'Fetch' in env_name or 'Hand' in env_name:
s = np.concatenate([s[key] for key in s.keys()])
# For n in range number of steps
for t in range(max_ep_steps):
# Given observations, get action value and neglopacs
# We already have self.obs because Runner superclass run self.obs[:] = env.reset() on init
[a], [value] = policy.choose_action(s)
action = np.tanh(a)
action = a_lowerbound + (action + 1.) * (a_upperbound -
a_lowerbound) / 2
# Run in simulator
s_, r, done, info = env.step(action)
if 'Fetch' in env_name or 'Hand' in env_name:
s_ = np.concatenate([s_[key] for key in s_.keys()])
if t == max_ep_steps - 1:
done = True
terminal = 1. if done else 0.
if Render:
env.render()
current_path['rewards'].append(r)
current_path['action'].append(a)
current_path['obs'].append(s)
current_path['obs_'].append(s_)
current_path['done'].append(terminal)
current_path['value'].append(value)
current_path['t'].append(t)
if done:
global_step += t + 1
last_training_paths.appendleft(current_path)
break
else:
s = s_
# mb_obs = np.asarray(mb_obs, dtype=s.dtype)
# mb_values = np.asarray(mb_values, dtype=s.dtype)
# mb_l_values = np.asarray(mb_l_values, dtype=s.dtype)
# mb_actions = np.asarray(mb_actions, dtype=action.dtype)
# mb_obs_ = np.asarray(mb_obs_, dtype=s_.dtype)
# mb_rewards = np.asarray(mb_rewards, dtype=np.float32)
# mb_l_rewards = np.asarray(mb_l_rewards, dtype=np.float32)
# mb_terminals = np.asarray(mb_terminals, dtype=np.float32)
# last_value, last_l_value = policy.predict_values([s_])
rescale = np.mean([len(path) for path in last_training_paths])
initial_return = []
mb_advs = []
for path in last_training_paths:
lastgaelam = 0
path_advs = np.zeros_like(path['rewards'])
path_values = path['value']
path_next_values = path['value'][1:]
path_next_values.append(policy.predict_values(path['obs_'][-1]))
for t in reversed(range(len(path_values))):
delta = path['rewards'][t] + gamma * path_next_values[t] * (
1 - path['done'][t]) - path_values[t]
path_advs[t] = lastgaelam = delta + gamma * gae_lamda * (
1 - path['done'][t]) * lastgaelam
path_returns = path_advs + path_values
initial_return.append(path_returns[0])
mb_advs.extend(path_advs)
mb_obs.extend(path['obs'])
mb_obs_.extend(path['obs_'])
mb_values.extend(path['value'])
mb_terminals.extend(path['done'])
mb_t.extend(path['t'])
mb_actions.extend(path['action'])
initial_return = np.asarray(initial_return, dtype=np.float32)
mb_obs = np.asarray(mb_obs, dtype=s.dtype)
mb_values = np.asarray(mb_values, dtype=s.dtype)
mb_actions = np.asarray(mb_actions, dtype=action.dtype)
mb_obs_ = np.asarray(mb_obs_, dtype=s_.dtype)
mb_rewards = np.asarray(mb_rewards, dtype=np.float32)
mb_terminals = np.asarray(mb_terminals, dtype=np.float32)
mb_advs = np.asarray(mb_advs, dtype=np.float32)
mb_t = np.asarray(mb_t, dtype=np.float32)
mb_returns = mb_advs + mb_values
mblossvals = []
inds = np.arange(len(mb_advs), dtype=int)
initial_return = np.mean(initial_return)
# Randomize the indexes
np.random.shuffle(inds)
# 0 to batch_size with batch_train_size step
# if sum(current_path['l_rewards'])>0:
# policy.ALPHA3 = min(policy.ALPHA3 * 1.5, policy_params['alpha3'])
# else:
# policy.ALPHA3 = min(policy.ALPHA3 * 1.01, policy_params['alpha3'])
slices = (arr[inds] for arr in (mb_obs, mb_obs_, mb_returns, mb_advs,
mb_actions, mb_values, mb_t))
# print(**slices)
mblossvals.append(
policy.update(*slices, initial_return, cliprangenow, lr_c_now,
rescale))
mblossvals = np.mean(mblossvals, axis=0)
frac = 1.0 - (global_step - 1.0) / max_global_steps
cliprangenow = cliprange * frac
lr_c_now = lr_c * frac # learning rate for critic
# lr_l_now = lr_l * frac # learning rate for critic
logger.logkv("total_timesteps", global_step)
training_diagnotic = evaluate_training_rollouts(last_training_paths)
if training_diagnotic is not None:
# [training_diagnotics[key].append(training_diagnotic[key]) for key in training_diagnotic.keys()]\
eval_diagnotic = training_evaluation(variant, evaluation_env,
policy)
[
logger.logkv(key, eval_diagnotic[key])
for key in eval_diagnotic.keys()
]
training_diagnotic.pop('return')
[
logger.logkv(key, training_diagnotic[key])
for key in training_diagnotic.keys()
]
logger.logkv('lr_c', lr_c_now)
[
logger.logkv(name, value)
for name, value in zip(policy.diagnosis_names, mblossvals)
]
string_to_print = ['time_step:', str(global_step), '|']
[
string_to_print.extend(
[key, ':', str(eval_diagnotic[key]), '|'])
for key in eval_diagnotic.keys()
]
[
string_to_print.extend(
[key, ':',
str(round(training_diagnotic[key], 2)), '|'])
for key in training_diagnotic.keys()
]
print(''.join(string_to_print))
logger.dumpkvs()
# 状态更新
# OUTPUT TRAINING INFORMATION AND LEARNING RATE DECAY
print('Running time: ', time.time() - t1)
return