def main():
env = DialogEnvironment()
experiment_name = args.logdir.split('/')[1] #model name
torch.manual_seed(args.seed)
#TODO
actor = Actor(hidden_size=args.hidden_size,num_layers=args.num_layers,device='cuda',input_size=args.input_size,output_size=args.input_size)
critic = Critic(hidden_size=args.hidden_size,num_layers=args.num_layers,input_size=args.input_size,seq_len=args.seq_len)
discrim = Discriminator(hidden_size=args.hidden_size,num_layers=args.hidden_size,input_size=args.input_size,seq_len=args.seq_len)
actor.to(device), critic.to(device), discrim.to(device)
actor_optim = optim.Adam(actor.parameters(), lr=args.learning_rate)
critic_optim = optim.Adam(critic.parameters(), lr=args.learning_rate,
weight_decay=args.l2_rate)
discrim_optim = optim.Adam(discrim.parameters(), lr=args.learning_rate)
# load demonstrations
writer = SummaryWriter(args.logdir)
if args.load_model is not None: #TODO
saved_ckpt_path = os.path.join(os.getcwd(), 'save_model', str(args.load_model))
ckpt = torch.load(saved_ckpt_path)
actor.load_state_dict(ckpt['actor'])
critic.load_state_dict(ckpt['critic'])
discrim.load_state_dict(ckpt['discrim'])
episodes = 0
train_discrim_flag = True
for iter in range(args.max_iter_num):
actor.eval(), critic.eval()
memory = deque()
steps = 0
scores = []
similarity_scores = []
while steps < args.total_sample_size:
scores = []
similarity_scores = []
state, expert_action, raw_state, raw_expert_action = env.reset()
score = 0
similarity_score = 0
state = state[:args.seq_len,:]
expert_action = expert_action[:args.seq_len,:]
state = state.to(device)
expert_action = expert_action.to(device)
for _ in range(10000):
steps += 1
mu, std = actor(state.resize(1,args.seq_len,args.input_size)) #TODO: gotta be a better way to resize.
action = get_action(mu.cpu(), std.cpu())[0]
for i in range(5):
emb_sum = expert_action[i,:].sum().cpu().item()
if emb_sum == 0:
# print(i)
action[i:,:] = 0 # manual padding
break
done= env.step(action)
irl_reward = get_reward(discrim, state, action, args)
if done:
mask = 0
else:
mask = 1
memory.append([state, torch.from_numpy(action).to(device), irl_reward, mask,expert_action])
score += irl_reward
similarity_score += get_cosine_sim(expert=expert_action,action=action.squeeze(),seq_len=5)
#print(get_cosine_sim(s1=expert_action,s2=action.squeeze(),seq_len=5),'sim')
if done:
break
episodes += 1
scores.append(score)
similarity_scores.append(similarity_score)
score_avg = np.mean(scores)
similarity_score_avg = np.mean(similarity_scores)
print('{}:: {} episode score is {:.2f}'.format(iter, episodes, score_avg))
print('{}:: {} episode similarity score is {:.2f}'.format(iter, episodes, similarity_score_avg))
actor.train(), critic.train(), discrim.train()
if train_discrim_flag:
expert_acc, learner_acc = train_discrim(discrim, memory, discrim_optim, args)
print("Expert: %.2f%% | Learner: %.2f%%" % (expert_acc * 100, learner_acc * 100))
writer.add_scalar('log/expert_acc', float(expert_acc), iter) #logg
writer.add_scalar('log/learner_acc', float(learner_acc), iter) #logg
writer.add_scalar('log/avg_acc', float(learner_acc + expert_acc)/2, iter) #logg
if args.suspend_accu_exp is not None: #only if not None do we check.
if expert_acc > args.suspend_accu_exp and learner_acc > args.suspend_accu_gen:
train_discrim_flag = False
train_actor_critic(actor, critic, memory, actor_optim, critic_optim, args)
writer.add_scalar('log/score', float(score_avg), iter)
writer.add_scalar('log/similarity_score', float(similarity_score_avg), iter)
writer.add_text('log/raw_state', raw_state[0],iter)
raw_action = get_raw_action(action) #TODO
writer.add_text('log/raw_action', raw_action,iter)
writer.add_text('log/raw_expert_action', raw_expert_action,iter)
if iter % 100:
score_avg = int(score_avg)
# Open a file with access mode 'a'
file_object = open(experiment_name+'.txt', 'a')
result_str = str(iter) + '|' + raw_state[0] + '|' + raw_action + '|' + raw_expert_action + '\n'
# Append at the end of file
file_object.write(result_str)
# Close the file
file_object.close()
model_path = os.path.join(os.getcwd(),'save_model')
if not os.path.isdir(model_path):
os.makedirs(model_path)
ckpt_path = os.path.join(model_path, experiment_name + '_ckpt_'+ str(score_avg)+'.pth.tar')
save_checkpoint({
'actor': actor.state_dict(),
'critic': critic.state_dict(),
'discrim': discrim.state_dict(),
'args': args,
'score': score_avg,
}, filename=ckpt_path)
def main():
env = DialogEnvironment()
experiment_name = args.logdir.split('/')[1] #model name
torch.manual_seed(args.seed)
#TODO
actor = Actor(hidden_size=args.hidden_size,num_layers=args.num_layers,device='cuda',input_size=args.input_size,output_size=args.input_size)
actor.to(device)
actor_optim = optim.Adam(actor.parameters(), lr=args.learning_rate)
# load demonstrations
writer = SummaryWriter(args.logdir)
if args.load_model is not None: #TODO
saved_ckpt_path = os.path.join(os.getcwd(), 'save_model', str(args.load_model))
ckpt = torch.load(saved_ckpt_path)
actor.load_state_dict(ckpt['actor'])
episodes = 0
for iter in range(args.max_iter_num):
actor.eval()
steps = 0
scores = []
states = []
expert_actions = []
while steps < args.batch_size:
scores = []
similarity_scores = []
state, expert_action, raw_state, raw_expert_action = env.reset()
score = 0
similarity_score = 0
state = state[:args.seq_len,:]
expert_action = expert_action[:args.seq_len,:]
state = state.to(device)
expert_action = expert_action.to(device)
states.append(state)
expert_actions.append(expert_action)
similarity_score += get_cosine_sim(expert=expert_action,action=action.squeeze(),seq_len=5)
#print(get_cosine_sim(s1=expert_action,s2=action.squeeze(),seq_len=5),'sim')
if done:
break
episodes += 1
similarity_scores.append(similarity_score)
states = torch.stack(states)
actions_pred , _ = actor(states)
expert_actions = torch.stack(expert_actions)
similarity_score_avg = np.mean(similarity_scores)
print('{}:: {} episode similarity score is {:.2f}'.format(iter, episodes, similarity_score_avg))
actor.train()
loss = F.mse_loss(actions_pred,expert_action)
actor_optim.zero_grad()
actor_optim.step()
# and this is basically all we need to do
train_actor_critic(actor, critic, memory, actor_optim, critic_optim, args)
writer.add_scalar('log/score', float(score_avg), iter)
writer.add_scalar('log/similarity_score', float(similarity_score_avg), iter)
writer.add_text('log/raw_state', raw_state[0],iter)
raw_action = get_raw_action(action) #TODO
writer.add_text('log/raw_action', raw_action,iter)
writer.add_text('log/raw_expert_action', raw_expert_action,iter)
if iter % 100:
score_avg = int(score_avg)
# Open a file with access mode 'a'
file_object = open(experiment_name+'.txt', 'a')
result_str = str(iter) + '|' + raw_state[0] + '|' + raw_action + '|' + raw_expert_action + '\n'
# Append at the end of file
file_object.write(result_str)
# Close the file
file_object.close()
model_path = os.path.join(os.getcwd(),'save_model')
if not os.path.isdir(model_path):
os.makedirs(model_path)
ckpt_path = os.path.join(model_path, experiment_name + '_ckpt_'+ str(score_avg)+'.pth.tar')
save_checkpoint({
'actor': actor.state_dict(),
'critic': critic.state_dict(),
'discrim': discrim.state_dict(),
'args': args,
'score': score_avg,
}, filename=ckpt_path)
class DDPGAgent():
def __init__(self, env, hp):
self.env = env
self.hp = hp
self.critic = Critic(env.observation_space.shape[0],
env.action_space.shape[0], hp)
self.target_critic = Critic(env.observation_space.shape[0],
env.action_space.shape[0], hp)
self.actor = Actor(env.observation_space.shape[0],
env.action_space.shape[0], env.action_space.high[0],
hp)
self.target_actor = Actor(env.observation_space.shape[0],
env.action_space.shape[0],
env.action_space.high[0], hp)
self.dataset = ReplayBuffer(self.hp['batch_size'],
self.hp['max_buffer_size'])
self.noise = OrnsteinUhlenbeckProcess(env.action_space.shape[0],
sigma=self.hp['noise_sigma'])
self.noise.reset_states()
def take_action(self, state, greedy=False):
state = Variable(torch.from_numpy(state)).float()
action = self.actor.predict(state)
if greedy:
return action.detach().numpy()
return action.detach().numpy() \
+ (self.noise.sample() * self.env.action_space.high[0])
def collect(self, n_episodes, max_episodes):
state = self.env.reset()
reward_list = []
for _ in range(n_episodes):
reward = 0
for step in range(max_episodes):
action = self.take_action(state, greedy=True)
s_next, r, done, _ = self.env.step(action)
state = s_next
reward += r
if done:
break
reward_list.append(reward)
state = self.env.reset()
return np.mean(reward_list)
def buffer_update(self, sample):
self.dataset.add_sample(sample)
def _critic_update(self, batch):
s = batch[0]
a = batch[1]
r = batch[2]
s_next = batch[3]
done = batch[4]
target_actions = self.target_actor.predict(s_next)
Q_val = self.target_critic.predict(s_next, target_actions)
y_target = r + done * (self.hp['gamma'] * Q_val)
#y_target2 = r + self.hp['gamma'] * Q_val
#print(y_target!=y_target2,done)
y_pred = self.critic.predict(s, a)
self.critic.train(y_pred, y_target)
def _actor_update(self, batch):
s = batch[0]
pred_a = self.actor.predict(s)
loss = torch.mean(-self.critic.predict(s, pred_a))
self.actor.train(loss)
def update(self):
if self.dataset.length < self.hp['batch_size']:
return
batch = self.dataset.get_batch()
self._critic_update(batch)
self._actor_update(batch)
self._target_update(self.hp['tau'], self.target_critic, self.critic)
self._target_update(self.hp['tau'], self.target_actor, self.actor)
def _target_update(self, tau, target_network, network):
for target_param, param in zip(target_network.parameters(),
network.parameters()):
target_param.data.copy_(tau * param.data + target_param.data *
(1.0 - tau))
def save_models(self, episode):
torch.save(self.target_actor.state_dict(),
'./trained_models/' + str(episode) + 'actor.pt')
torch.save(self.target_critic.state_dict(),
'./trained_models/' + str(episode) + 'critic.pt')
print('Models Saved!')
def load_models(self, path):
self.actor.load_state_dict(torch.load(path + 'actor.pt'))
self.critic.load_state_dict(torch.load(path + 'critic.pt'))
self._target_update(1, self.target_actor, self.actor)
self._target_update(1, self.target_critic, self.critic)
print('Models Loaded!')
