def __init__(self, is_train=False, dataset='Multiwoz'): with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.json'), 'r') as f: cfg = json.load(f) self.save_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), cfg['save_dir']) self.save_per_epoch = cfg['save_per_epoch'] self.update_round = cfg['update_round'] self.optim_batchsz = cfg['batchsz'] self.gamma = cfg['gamma'] self.epsilon = cfg['epsilon'] self.tau = cfg['tau'] self.is_train = is_train if is_train: init_logging_handler(os.path.join(os.path.dirname(os.path.abspath(__file__)), cfg['log_dir'])) # construct policy and value network if dataset == 'Multiwoz': voc_file = os.path.join(root_dir, 'data/multiwoz/sys_da_voc.txt') voc_opp_file = os.path.join(root_dir, 'data/multiwoz/usr_da_voc.txt') self.vector = MultiWozVector(voc_file, voc_opp_file) self.policy = MultiDiscretePolicy(self.vector.state_dim, cfg['h_dim'], self.vector.da_dim).to(device=DEVICE) self.value = Value(self.vector.state_dim, cfg['hv_dim']).to(device=DEVICE) if is_train: self.policy_optim = optim.RMSprop(self.policy.parameters(), lr=cfg['policy_lr']) self.value_optim = optim.Adam(self.value.parameters(), lr=cfg['value_lr'])
class GDPL(Policy): def __init__(self, is_train=False, dataset='Multiwoz'): with open( os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.json'), 'r') as f: cfg = json.load(f) self.save_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), cfg['save_dir']) self.save_per_epoch = cfg['save_per_epoch'] self.update_round = cfg['update_round'] self.optim_batchsz = cfg['batchsz'] self.gamma = cfg['gamma'] self.epsilon = cfg['epsilon'] self.tau = cfg['tau'] self.is_train = is_train if is_train: init_logging_handler( os.path.join(os.path.dirname(os.path.abspath(__file__)), cfg['log_dir'])) # construct policy and value network if dataset == 'Multiwoz': voc_file = os.path.join(root_dir, 'data/multiwoz/sys_da_voc.txt') voc_opp_file = os.path.join(root_dir, 'data/multiwoz/usr_da_voc.txt') self.vector = MultiWozVector(voc_file, voc_opp_file) self.policy = MultiDiscretePolicy( self.vector.state_dim, cfg['h_dim'], self.vector.da_dim).to(device=DEVICE) self.value = Value(self.vector.state_dim, cfg['hv_dim']).to(device=DEVICE) if is_train: self.policy_optim = optim.RMSprop(self.policy.parameters(), lr=cfg['lr']) self.value_optim = optim.Adam(self.value.parameters(), lr=cfg['lr']) def predict(self, state): """ Predict an system action given state. Args: state (dict): Dialog state. Please refer to util/state.py Returns: action : System act, with the form of (act_type, {slot_name_1: value_1, slot_name_2, value_2, ...}) """ s_vec = torch.Tensor(self.vector.state_vectorize(state)) a = self.policy.select_action(s_vec.to(device=DEVICE), self.is_train).cpu() return self.vector.action_devectorize(a.numpy()) def init_session(self): """ Restore after one session """ pass def est_adv(self, r, v, mask): """ we save a trajectory in continuous space and it reaches the ending of current trajectory when mask=0. :param r: reward, Tensor, [b] :param v: estimated value, Tensor, [b] :param mask: indicates ending for 0 otherwise 1, Tensor, [b] :return: A(s, a), V-target(s), both Tensor """ batchsz = v.size(0) # v_target is worked out by Bellman equation. v_target = torch.Tensor(batchsz).to(device=DEVICE) delta = torch.Tensor(batchsz).to(device=DEVICE) A_sa = torch.Tensor(batchsz).to(device=DEVICE) prev_v_target = 0 prev_v = 0 prev_A_sa = 0 for t in reversed(range(batchsz)): # mask here indicates a end of trajectory # this value will be treated as the target value of value network. # mask = 0 means the immediate reward is the real V(s) since it's end of trajectory. # formula: V(s_t) = r_t + gamma * V(s_t+1) v_target[t] = r[t] + self.gamma * prev_v_target * mask[t] # please refer to : https://arxiv.org/abs/1506.02438 # for generalized adavantage estimation # formula: delta(s_t) = r_t + gamma * V(s_t+1) - V(s_t) delta[t] = r[t] + self.gamma * prev_v * mask[t] - v[t] # formula: A(s, a) = delta(s_t) + gamma * lamda * A(s_t+1, a_t+1) # here use symbol tau as lambda, but original paper uses symbol lambda. A_sa[t] = delta[t] + self.gamma * self.tau * prev_A_sa * mask[t] # update previous prev_v_target = v_target[t] prev_v = v[t] prev_A_sa = A_sa[t] # normalize A_sa A_sa = (A_sa - A_sa.mean()) / A_sa.std() return A_sa, v_target def update(self, epoch, batchsz, s, a, next_s, mask, rewarder): # update reward estimator rewarder.update_irl((s, a, next_s), batchsz, epoch) # get estimated V(s) and PI_old(s, a) # actually, PI_old(s, a) can be saved when interacting with env, so as to save the time of one forward elapsed # v: [b, 1] => [b] v = self.value(s).squeeze(-1).detach() log_pi_old_sa = self.policy.get_log_prob(s, a).detach() # estimate advantage and v_target according to GAE and Bellman Equation r = rewarder.estimate(s, a, next_s, log_pi_old_sa).detach() A_sa, v_target = self.est_adv(r, v, mask) for i in range(self.update_round): # 1. shuffle current batch perm = torch.randperm(batchsz) # shuffle the variable for mutliple optimize v_target_shuf, A_sa_shuf, s_shuf, a_shuf, log_pi_old_sa_shuf = v_target[perm], A_sa[perm], s[perm], a[perm], \ log_pi_old_sa[perm] # 2. get mini-batch for optimizing optim_chunk_num = int(np.ceil(batchsz / self.optim_batchsz)) # chunk the optim_batch for total batch v_target_shuf, A_sa_shuf, s_shuf, a_shuf, log_pi_old_sa_shuf = torch.chunk(v_target_shuf, optim_chunk_num), \ torch.chunk(A_sa_shuf, optim_chunk_num), \ torch.chunk(s_shuf, optim_chunk_num), \ torch.chunk(a_shuf, optim_chunk_num), \ torch.chunk(log_pi_old_sa_shuf, optim_chunk_num) # 3. iterate all mini-batch to optimize policy_loss, value_loss = 0., 0. for v_target_b, A_sa_b, s_b, a_b, log_pi_old_sa_b in zip( v_target_shuf, A_sa_shuf, s_shuf, a_shuf, log_pi_old_sa_shuf): # print('optim:', batchsz, v_target_b.size(), A_sa_b.size(), s_b.size(), a_b.size(), log_pi_old_sa_b.size()) # 1. update value network self.value_optim.zero_grad() v_b = self.value(s_b).squeeze(-1) loss = (v_b - v_target_b).pow(2).mean() value_loss += loss.item() # backprop loss.backward() # nn.utils.clip_grad_norm(self.value.parameters(), 4) self.value_optim.step() # 2. update policy network by clipping self.policy_optim.zero_grad() # [b, 1] log_pi_sa = self.policy.get_log_prob(s_b, a_b) # ratio = exp(log_Pi(a|s) - log_Pi_old(a|s)) = Pi(a|s) / Pi_old(a|s) # we use log_pi for stability of numerical operation # [b, 1] => [b] ratio = (log_pi_sa - log_pi_old_sa_b).exp().squeeze(-1) surrogate1 = ratio * A_sa_b surrogate2 = torch.clamp(ratio, 1 - self.epsilon, 1 + self.epsilon) * A_sa_b # this is element-wise comparing. # we add negative symbol to convert gradient ascent to gradient descent surrogate = -torch.min(surrogate1, surrogate2).mean() policy_loss += surrogate.item() # backprop surrogate.backward() # gradient clipping, for stability torch.nn.utils.clip_grad_norm(self.policy.parameters(), 10) # self.lock.acquire() # retain lock to update weights self.policy_optim.step() # self.lock.release() # release lock value_loss /= optim_chunk_num policy_loss /= optim_chunk_num logging.debug( '<<dialog policy ppo>> epoch {}, iteration {}, value, loss {}'. format(epoch, i, value_loss)) logging.debug( '<<dialog policy ppo>> epoch {}, iteration {}, policy, loss {}' .format(epoch, i, policy_loss)) if (epoch + 1) % self.save_per_epoch == 0: self.save(self.save_dir, epoch) def save(self, directory, epoch): if not os.path.exists(directory): os.makedirs(directory) torch.save(self.value.state_dict(), directory + '/' + str(epoch) + '_ppo.val.mdl') torch.save(self.policy.state_dict(), directory + '/' + str(epoch) + '_ppo.pol.mdl') logging.info( '<<dialog policy>> epoch {}: saved network to mdl'.format(epoch)) def load(self, filename): value_mdl = os.path.join(os.path.dirname(os.path.abspath(__file__)), filename + '_ppo.val.mdl') if os.path.exists(value_mdl): self.value.load_state_dict(torch.load(value_mdl)) print('<<dialog policy>> loaded checkpoint from file: {}'.format( value_mdl)) policy_mdl = os.path.join(os.path.dirname(os.path.abspath(__file__)), filename + '_ppo.pol.mdl') if os.path.exists(policy_mdl): self.policy.load_state_dict(torch.load(policy_mdl)) print('<<dialog policy>> loaded checkpoint from file: {}'.format( policy_mdl))