class DDPG:
def __init__(self, env, args):
ob_space = env.observation_space
goal_dim = env.goal_dim
ob_dim = ob_space.shape[0]
self.ob_dim = ob_dim
self.ac_dim = ac_dim = 7
self.goal_dim = goal_dim
self.num_iters = args.num_iters
self.random_prob = args.random_prob
self.tau = args.tau
self.reward_scale = args.reward_scale
self.gamma = args.gamma
self.log_interval = args.log_interval
self.save_interval = args.save_interval
self.rollout_steps = args.rollout_steps
self.env = env
self.batch_size = args.batch_size
self.train_steps = args.train_steps
self.closest_dist = np.inf
self.warmup_iter = args.warmup_iter
self.max_grad_norm = args.max_grad_norm
self.use_her = args.her
self.k_future = args.k_future
self.model_dir = os.path.join(args.save_dir, 'model')
self.pretrain_dir = args.pretrain_dir
os.makedirs(self.model_dir, exist_ok=True)
self.global_step = 0
self.actor = Actor(ob_dim=ob_dim,
act_dim=ac_dim,
hid1_dim=args.hid1_dim,
hid2_dim=args.hid2_dim,
hid3_dim=args.hid3_dim,
init_method=args.init_method)
self.critic = Critic(ob_dim=ob_dim,
act_dim=ac_dim,
hid1_dim=args.hid1_dim,
hid2_dim=args.hid2_dim,
hid3_dim=args.hid3_dim,
init_method=args.init_method)
if args.resume or args.test or args.pretrain_dir is not None:
self.load_model(args.resume_step, pretrain_dir=args.pretrain_dir)
if not args.test:
self.actor_target = Actor(ob_dim=ob_dim,
act_dim=ac_dim,
hid1_dim=args.hid1_dim,
hid2_dim=args.hid2_dim,
hid3_dim=args.hid3_dim,
init_method=args.init_method)
self.critic_target = Critic(ob_dim=ob_dim,
act_dim=ac_dim,
hid1_dim=args.hid1_dim,
hid2_dim=args.hid2_dim,
hid3_dim=args.hid3_dim,
init_method=args.init_method)
self.actor_optim = self.construct_optim(self.actor,
lr=args.actor_lr)
cri_w_decay = args.critic_weight_decay
self.critic_optim = self.construct_optim(self.critic,
lr=args.critic_lr,
weight_decay=cri_w_decay)
self.hard_update(self.actor_target, self.actor)
self.hard_update(self.critic_target, self.critic)
self.actor_target.eval()
self.critic_target.eval()
if args.noise_type == 'ou_noise':
mu = np.zeros(ac_dim)
sigma = float(args.ou_noise_std) * np.ones(ac_dim)
self.action_noise = OrnsteinUhlenbeckActionNoise(mu=mu,
sigma=sigma)
elif args.noise_type == 'uniform':
low_limit = args.uniform_noise_low
high_limit = args.uniform_noise_high
dec_step = args.max_noise_dec_step
self.action_noise = UniformNoise(low_limit=low_limit,
high_limit=high_limit,
dec_step=dec_step)
elif args.noise_type == 'gaussian':
mu = np.zeros(ac_dim)
sigma = args.normal_noise_std * np.ones(ac_dim)
self.action_noise = NormalActionNoise(mu=mu, sigma=sigma)
self.memory = Memory(limit=int(args.memory_limit),
action_shape=(int(ac_dim), ),
observation_shape=(int(ob_dim), ))
self.critic_loss = nn.MSELoss()
self.ob_norm = args.ob_norm
if self.ob_norm:
self.obs_oms = OnlineMeanStd(shape=(1, ob_dim))
else:
self.obs_oms = None
self.cuda()
def test(self, render=False, record=True, slow_t=0):
dist, succ_rate = self.rollout(render=render,
record=record,
slow_t=slow_t)
print('Final step distance: ', dist)
def train(self):
self.net_mode(train=True)
tfirststart = time.time()
epoch_episode_rewards = deque(maxlen=1)
epoch_episode_steps = deque(maxlen=1)
total_rollout_steps = 0
for epoch in range(self.global_step, self.num_iters):
episode_reward = 0
episode_step = 0
self.action_noise.reset()
obs = self.env.reset()
obs = obs[0]
epoch_actor_losses = []
epoch_critic_losses = []
if self.use_her:
ep_experi = {
'obs': [],
'act': [],
'reward': [],
'new_obs': [],
'ach_goals': [],
'done': []
}
for t_rollout in range(self.rollout_steps):
total_rollout_steps += 1
ran = np.random.random(1)[0]
if self.pretrain_dir is None and epoch < self.warmup_iter or \
ran < self.random_prob:
act = self.random_action().flatten()
else:
act = self.policy(obs).flatten()
new_obs, r, done, info = self.env.step(act)
ach_goals = new_obs[1].copy()
new_obs = new_obs[0].copy()
episode_reward += r
episode_step += 1
self.memory.append(obs, act, r * self.reward_scale, new_obs,
ach_goals, done)
if self.use_her:
ep_experi['obs'].append(obs)
ep_experi['act'].append(act)
ep_experi['reward'].append(r * self.reward_scale)
ep_experi['new_obs'].append(new_obs)
ep_experi['ach_goals'].append(ach_goals)
ep_experi['done'].append(done)
if self.ob_norm:
self.obs_oms.update(new_obs)
obs = new_obs
epoch_episode_rewards.append(episode_reward)
epoch_episode_steps.append(episode_step)
if self.use_her:
for t in range(episode_step - self.k_future):
ob = ep_experi['obs'][t]
act = ep_experi['act'][t]
new_ob = ep_experi['new_obs'][t]
ach_goal = ep_experi['ach_goals'][t]
k_futures = np.random.choice(np.arange(
t + 1, episode_step),
self.k_future - 1,
replace=False)
k_futures = np.concatenate((np.array([t]), k_futures))
for future in k_futures:
new_goal = ep_experi['ach_goals'][future]
her_ob = np.concatenate(
(ob[:-self.goal_dim], new_goal), axis=0)
her_new_ob = np.concatenate(
(new_ob[:-self.goal_dim], new_goal), axis=0)
res = self.env.cal_reward(ach_goal.copy(), new_goal,
act)
her_reward, _, done = res
self.memory.append(her_ob, act,
her_reward * self.reward_scale,
her_new_ob, ach_goal.copy(), done)
self.global_step += 1
if epoch >= self.warmup_iter:
for t_train in range(self.train_steps):
act_loss, cri_loss = self.train_net()
epoch_critic_losses.append(cri_loss)
epoch_actor_losses.append(act_loss)
if epoch % self.log_interval == 0:
tnow = time.time()
stats = {}
if self.ob_norm:
stats['ob_oms_mean'] = safemean(self.obs_oms.mean.numpy())
stats['ob_oms_std'] = safemean(self.obs_oms.std.numpy())
stats['total_rollout_steps'] = total_rollout_steps
stats['rollout/return'] = safemean(
[rew for rew in epoch_episode_rewards])
stats['rollout/ep_steps'] = safemean(
[l for l in epoch_episode_steps])
if epoch >= self.warmup_iter:
stats['actor_loss'] = np.mean(epoch_actor_losses)
stats['critic_loss'] = np.mean(epoch_critic_losses)
stats['epoch'] = epoch
stats['actor_lr'] = self.actor_optim.param_groups[0]['lr']
stats['critic_lr'] = self.critic_optim.param_groups[0]['lr']
stats['time_elapsed'] = tnow - tfirststart
for name, value in stats.items():
logger.logkv(name, value)
logger.dumpkvs()
if (epoch == 0 or epoch >= self.warmup_iter) and \
self.save_interval and\
epoch % self.save_interval == 0 and \
logger.get_dir():
mean_final_dist, succ_rate = self.rollout()
logger.logkv('epoch', epoch)
logger.logkv('test/total_rollout_steps', total_rollout_steps)
logger.logkv('test/mean_final_dist', mean_final_dist)
logger.logkv('test/succ_rate', succ_rate)
tra_mean_dist, tra_succ_rate = self.rollout(train_test=True)
logger.logkv('train/mean_final_dist', tra_mean_dist)
logger.logkv('train/succ_rate', tra_succ_rate)
# self.log_model_weights()
logger.dumpkvs()
if mean_final_dist < self.closest_dist:
self.closest_dist = mean_final_dist
is_best = True
else:
is_best = False
self.save_model(is_best=is_best, step=self.global_step)
def train_net(self):
batch_data = self.memory.sample(batch_size=self.batch_size)
for key, value in batch_data.items():
batch_data[key] = torch.from_numpy(value)
obs0_t = batch_data['obs0']
obs1_t = batch_data['obs1']
obs0_t = self.normalize(obs0_t, self.obs_oms)
obs1_t = self.normalize(obs1_t, self.obs_oms)
obs0 = Variable(obs0_t).float().cuda()
with torch.no_grad():
vol_obs1 = Variable(obs1_t).float().cuda()
rewards = Variable(batch_data['rewards']).float().cuda()
actions = Variable(batch_data['actions']).float().cuda()
terminals = Variable(batch_data['terminals1']).float().cuda()
cri_q_val = self.critic(obs0, actions)
with torch.no_grad():
target_net_act = self.actor_target(vol_obs1)
target_net_q_val = self.critic_target(vol_obs1, target_net_act)
# target_net_q_val.volatile = False
target_q_label = rewards
target_q_label += self.gamma * target_net_q_val * (1 - terminals)
target_q_label = target_q_label.detach()
self.actor.zero_grad()
self.critic.zero_grad()
cri_loss = self.critic_loss(cri_q_val, target_q_label)
cri_loss.backward()
if self.max_grad_norm is not None:
torch.nn.utils.clip_grad_norm(self.critic.parameters(),
self.max_grad_norm)
self.critic_optim.step()
self.critic.zero_grad()
self.actor.zero_grad()
net_act = self.actor(obs0)
net_q_val = self.critic(obs0, net_act)
act_loss = -net_q_val.mean()
act_loss.backward()
if self.max_grad_norm is not None:
torch.nn.utils.clip_grad_norm(self.actor.parameters(),
self.max_grad_norm)
self.actor_optim.step()
self.soft_update(self.actor_target, self.actor, self.tau)
self.soft_update(self.critic_target, self.critic, self.tau)
return act_loss.cpu().data.numpy(), cri_loss.cpu().data.numpy()
def normalize(self, x, stats):
if stats is None:
return x
return (x - stats.mean) / stats.std
def denormalize(self, x, stats):
if stats is None:
return x
return x * stats.std + stats.mean
def net_mode(self, train=True):
if train:
self.actor.train()
self.critic.train()
else:
self.actor.eval()
self.critic.eval()
def load_model(self, step=None, pretrain_dir=None):
model_dir = self.model_dir
if pretrain_dir is not None:
ckpt_file = os.path.join(self.pretrain_dir, 'model_best.pth')
else:
if step is None:
ckpt_file = os.path.join(model_dir, 'model_best.pth')
else:
ckpt_file = os.path.join(model_dir,
'ckpt_{:08d}.pth'.format(step))
if not os.path.isfile(ckpt_file):
raise ValueError("No checkpoint found at '{}'".format(ckpt_file))
mutils.print_yellow('Loading checkpoint {}'.format(ckpt_file))
checkpoint = torch.load(ckpt_file)
if pretrain_dir is not None:
actor_dict = self.actor.state_dict()
critic_dict = self.critic.state_dict()
actor_pretrained_dict = {
k: v
for k, v in checkpoint['actor_state_dict'].items()
if k in actor_dict
}
critic_pretrained_dict = {
k: v
for k, v in checkpoint['critic_state_dict'].items()
if k in critic_dict
}
actor_dict.update(actor_pretrained_dict)
critic_dict.update(critic_pretrained_dict)
self.actor.load_state_dict(actor_dict)
self.critic.load_state_dict(critic_dict)
self.global_step = 0
else:
self.actor.load_state_dict(checkpoint['actor_state_dict'])
self.critic.load_state_dict(checkpoint['critic_state_dict'])
self.global_step = checkpoint['global_step']
if step is None:
mutils.print_yellow('Checkpoint step: {}'
''.format(checkpoint['ckpt_step']))
self.warmup_iter += self.global_step
mutils.print_yellow('Checkpoint loaded...')
def save_model(self, is_best, step=None):
if step is None:
step = self.global_step
ckpt_file = os.path.join(self.model_dir,
'ckpt_{:08d}.pth'.format(step))
data_to_save = {
'ckpt_step': step,
'global_step': self.global_step,
'actor_state_dict': self.actor.state_dict(),
'actor_optimizer': self.actor_optim.state_dict(),
'critic_state_dict': self.critic.state_dict(),
'critic_optimizer': self.critic_optim.state_dict()
}
mutils.print_yellow('Saving checkpoint: %s' % ckpt_file)
torch.save(data_to_save, ckpt_file)
if is_best:
torch.save(data_to_save,
os.path.join(self.model_dir, 'model_best.pth'))
def rollout(self, train_test=False, render=False, record=False, slow_t=0):
test_conditions = self.env.train_test_conditions \
if train_test else self.env.test_conditions
done_num = 0
final_dist = []
episode_length = []
for idx in range(test_conditions):
if train_test:
obs = self.env.train_test_reset(cond=idx)
else:
obs = self.env.test_reset(cond=idx)
for t_rollout in range(self.rollout_steps):
obs = obs[0].copy()
act = self.policy(obs, stochastic=False).flatten()
obs, r, done, info = self.env.step(act)
if render:
self.env.render()
if slow_t > 0:
time.sleep(slow_t)
if done:
done_num += 1
break
if record:
print('dist: ', info['dist'])
final_dist.append(info['dist'])
episode_length.append(t_rollout)
final_dist = np.array(final_dist)
mean_final_dist = np.mean(final_dist)
succ_rate = done_num / float(test_conditions)
if record:
with open('./test_data.json', 'w') as f:
json.dump(final_dist.tolist(), f)
print('\nDist statistics:')
print("Minimum: {0:9.4f} Maximum: {1:9.4f}"
"".format(np.min(final_dist), np.max(final_dist)))
print("Mean: {0:9.4f}".format(mean_final_dist))
print("Standard Deviation: {0:9.4f}".format(np.std(final_dist)))
print("Median: {0:9.4f}".format(np.median(final_dist)))
print("First quartile: {0:9.4f}"
"".format(np.percentile(final_dist, 25)))
print("Third quartile: {0:9.4f}"
"".format(np.percentile(final_dist, 75)))
print('Success rate:', succ_rate)
if render:
while True:
self.env.render()
return mean_final_dist, succ_rate
def log_model_weights(self):
for name, param in self.actor.named_parameters():
logger.logkv('actor/' + name, param.clone().cpu().data.numpy())
for name, param in self.actor_target.named_parameters():
logger.logkv('actor_target/' + name,
param.clone().cpu().data.numpy())
for name, param in self.critic.named_parameters():
logger.logkv('critic/' + name, param.clone().cpu().data.numpy())
for name, param in self.critic_target.named_parameters():
logger.logkv('critic_target/' + name,
param.clone().cpu().data.numpy())
def random_action(self):
act = np.random.uniform(-1., 1., self.ac_dim)
return act
def policy(self, obs, stochastic=True):
self.actor.eval()
ob = Variable(torch.from_numpy(obs)).float().cuda().view(1, -1)
act = self.actor(ob)
act = act.cpu().data.numpy()
if stochastic:
act = self.action_noise(act)
self.actor.train()
return act
def cuda(self):
self.critic.cuda()
self.actor.cuda()
if hasattr(self, 'critic_target'):
self.critic_target.cuda()
self.actor_target.cuda()
self.critic_loss.cuda()
def construct_optim(self, net, lr, weight_decay=None):
if weight_decay is None:
weight_decay = 0
params = mutils.add_weight_decay([net], weight_decay=weight_decay)
optimizer = optim.Adam(params, lr=lr, weight_decay=weight_decay)
return optimizer
def soft_update(self, target, source, tau):
for target_param, param in zip(target.parameters(),
source.parameters()):
target_param.data.copy_(target_param.data * (1.0 - tau) +
param.data * tau)
def hard_update(self, target, source):
for target_param, param in zip(target.parameters(),
source.parameters()):
target_param.data.copy_(param.data)
class DDPG:
def __init__(self, **kwargs):
if 'filename' in kwargs.keys():
data= torch.load(kwargs['filename'])
self.config= data["config"]
self.scores= data["scores"]
elif 'config' in kwargs.keys():
self.config= kwargs['config']
data= {}
self.scores= []
else:
raise OSError('DDPG: no configuration parameter in class init')
self.state_size = self.config["state_size"]
self.action_size = self.config["action_size"]
memory_size = self.config["memory_size"]
actor_lr = self.config["actor_lr"]
critic_lr = self.config["critic_lr"]
self.batch_size = self.config["batch_size"]
self.discount = self.config["discount"]
sigma = self.config["sigma"] if self.config["sigma"] else 0.2
self.tau= self.config["tau"]
self.seed = self.config["seed"] if self.config["seed"] else 0
self.action_noise= self.config["action_noise"] if self.config["action_noise"] else "No"
self.critic_l2_reg= self.config["critic_l2_reg"] if self.config["critic_l2_reg"] else 0.0
random.seed(self.seed)
torch.manual_seed(self.seed)
self.actor = Actor(self.state_size, self.action_size, nodes= self.config["actor_nodes"], seed= self.seed).to(device)
if 'actor' in data.keys(): self.actor.load_state_dict(data['actor'])
self.critic = Critic(self.state_size, self.action_size, nodes= self.config["critic_nodes"], seed= self.seed).to(device)
self.targetActor = Actor(self.state_size, self.action_size, nodes= self.config["actor_nodes"], seed= self.seed).to(device)
self.targetCritic = Critic(self.state_size, self.action_size, nodes= self.config["critic_nodes"], seed= self.seed).to(device)
# Initialize parameters
self.hard_update(self.actor, self.targetActor)
self.hard_update(self.critic, self.targetCritic)
self.actor_optimizer = optim.Adam(self.actor.parameters(), lr= actor_lr)
self.critic_optimizer = optim.Adam(self.critic.parameters(), lr= critic_lr, weight_decay= self.critic_l2_reg)
self.criticLoss = nn.MSELoss()
self.noise= None
if self.action_noise== "OU":
self.noise = OUNoise(np.zeros(self.action_size), sigma= sigma)
elif self.action_noise== "No":
self.noise = NoNoise()
elif self.action_noise== "Normal":
self.noise = NormalActionNoise(np.zeros(self.action_size), sigma= sigma)
self.memory = ReplayBuffer(self.action_size, memory_size, self.batch_size, self.seed)
def hard_update(self, source, target):
for target_param, param in zip(target.parameters(), source.parameters()):
target_param.data.copy_(param.data)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model: PyTorch model (weights will be copied from)
target_model: PyTorch model (weights will be copied to)
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)
def act(self, state, add_noise= True):
"""Returns actions for given state as per current policy."""
#state = torch.from_numpy(state).float().to(device)
#state= torch.FloatTensor(state).view(1, -1).to(device)
#state= torch.FloatTensor(state).unsqueeze(0).to(device)
state= torch.FloatTensor(state).to(device)
if len(state.size())== 1:
state= state.unsqueeze(0)
self.actor.eval()
with torch.no_grad():
action = self.actor(state).cpu().data.numpy()
self.actor.train()
if add_noise and self.noise:
action += self.noise()
return np.clip(action, -1, 1)
def step(self, state, action, reward, next_state, done):
"""Save experience in replay memory, and use random sample from buffer to learn."""
self.memory.add(state, action, reward, next_state, done)
if len(self.memory) >= self.batch_size:
self.learn()
def learn(self):
states, actions, rewards, next_states, dones = self.memory.sample()
# ---------------------------- update critic ---------------------------- #
# Get predicted next-state actions and Q values from target models
actions_next = self.targetActor(next_states)
Q_targets_next = self.targetCritic(next_states, actions_next)
# Compute Q targets for current states (y_i)
Q_targets = rewards + (self.discount * Q_targets_next * (1 - dones))
Q_targets = Variable(Q_targets.data, requires_grad=False)
# Compute critic loss
Q_expected = self.critic(states, actions)
critic_loss = self.criticLoss(Q_expected, Q_targets)
# Minimize the loss
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# ---------------------------- update actor ---------------------------- #
# Compute actor loss
actions_pred = self.actor(states)
actor_loss = -self.critic(states, actions_pred).mean()
# Minimize the loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# ----------------------- update target networks ----------------------- #
self.soft_update(self.critic, self.targetCritic, self.tau)
self.soft_update(self.actor, self.targetActor, self.tau)
def reset(self):
self.noise.reset()
def update(self, score= None):
if score: self.scores.append(score)
def save(self, filename= None):
data= {"config": self.config, "actor": self.actor.state_dict(), "scores": self.scores,}
if not filename:
filename= self.__class__.__name__+ '_'+ datetime.now().strftime("%Y-%m-%d_%H:%M:%S")+ '.data'
torch.save(data, filename)
torch.save(self.actor.state_dict(), "last_actor.pth")
#eval_env.seed(args.seed+100)
if logger.get_dir():
eval_env = bench.Monitor(
eval_env, os.path.join(logger.get_dir(), "eval.monitor.json"))
max_timesteps = train_env.spec.timestep_limit
# set noise type
current_noise_type = args.noise_type.strip()
nb_actions = train_env.action_space.shape[0]
if 'normal' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = NormalActionNoise(mu=np.zeros(nb_actions),
sigma=float(stddev) *
np.ones(nb_actions))
action_noise.reset()
if 'ou' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = OrnsteinUhlenbeckActionNoise(mu=np.zeros(nb_actions),
sigma=float(stddev) *
np.ones(nb_actions))
action_noise.reset()
else:
raise RuntimeError(
'unknown noise type "{}"'.format(current_noise_type))
episode_rewards = []
if 'Sparse' in train_env.spec.id:
sparse = True
episode_successes = []
else: