sac_ae_if/sac_ae.py

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import copy
import math

import utils
from encoder import make_encoder, club_loss, TransitionModel
from decoder import make_decoder

LOG_FREQ = 10000


def gaussian_logprob(noise, log_std):
    """Compute Gaussian log probability."""
    residual = (-0.5 * noise.pow(2) - log_std).sum(-1, keepdim=True)
    return residual - 0.5 * np.log(2 * np.pi) * noise.size(-1)


def squash(mu, pi, log_pi):
    """Apply squashing function.
    See appendix C from https://arxiv.org/pdf/1812.05905.pdf.
    """
    mu = torch.tanh(mu)
    if pi is not None:
        pi = torch.tanh(pi)
    if log_pi is not None:
        log_pi -= torch.log(F.relu(1 - pi.pow(2)) + 1e-6).sum(-1, keepdim=True)
    return mu, pi, log_pi


def weight_init(m):
    """Custom weight init for Conv2D and Linear layers."""
    if isinstance(m, nn.Linear):
        nn.init.orthogonal_(m.weight.data)
        m.bias.data.fill_(0.0)
    elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
        # delta-orthogonal init from https://arxiv.org/pdf/1806.05393.pdf
        assert m.weight.size(2) == m.weight.size(3)
        m.weight.data.fill_(0.0)
        m.bias.data.fill_(0.0)
        mid = m.weight.size(2) // 2
        gain = nn.init.calculate_gain('relu')
        nn.init.orthogonal_(m.weight.data[:, :, mid, mid], gain)


class Actor(nn.Module):
    """MLP actor network."""
    def __init__(
        self, obs_shape, action_shape, hidden_dim, encoder_type,
        encoder_feature_dim, log_std_min, log_std_max, num_layers, num_filters
    ):
        super().__init__()

        self.encoder = make_encoder(
            encoder_type, obs_shape, encoder_feature_dim, num_layers,
            num_filters
        )

        self.log_std_min = log_std_min
        self.log_std_max = log_std_max

        self.trunk = nn.Sequential(
            nn.Linear(self.encoder.feature_dim, hidden_dim), nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim), nn.ReLU(),
            nn.Linear(hidden_dim, 2 * action_shape[0])
        )

        self.outputs = dict()
        self.apply(weight_init)

    def forward(self, obs, compute_pi=True, compute_log_pi=True, detach_encoder=False):
        obs, _, _ = self.encoder(obs, detach=detach_encoder)

        mu, log_std = self.trunk(obs).chunk(2, dim=-1)

        # constrain log_std inside [log_std_min, log_std_max]
        log_std = torch.tanh(log_std)
        log_std = self.log_std_min + 0.5 * (
            self.log_std_max - self.log_std_min
        ) * (log_std + 1)

        self.outputs['mu'] = mu
        self.outputs['std'] = log_std.exp()

        if compute_pi:
            std = log_std.exp()
            noise = torch.randn_like(mu)
            pi = mu + noise * std
        else:
            pi = None
            entropy = None

        if compute_log_pi:
            log_pi = gaussian_logprob(noise, log_std)
        else:
            log_pi = None

        mu, pi, log_pi = squash(mu, pi, log_pi)
        return mu, pi, log_pi, log_std

    def log(self, L, step, log_freq=LOG_FREQ):
        if step % log_freq != 0:
            return

        for k, v in self.outputs.items():
            L.log_histogram('train_actor/%s_hist' % k, v, step)

        L.log_param('train_actor/fc1', self.trunk[0], step)
        L.log_param('train_actor/fc2', self.trunk[2], step)
        L.log_param('train_actor/fc3', self.trunk[4], step)


class QFunction(nn.Module):
    """MLP for q-function."""
    def __init__(self, obs_dim, action_dim, hidden_dim):
        super().__init__()

        self.trunk = nn.Sequential(
            nn.Linear(obs_dim + action_dim, hidden_dim), nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim), nn.ReLU(),
            nn.Linear(hidden_dim, 1)
        )

    def forward(self, obs, action):
        assert obs.size(0) == action.size(0)

        obs_action = torch.cat([obs, action], dim=1)
        return self.trunk(obs_action)


class Critic(nn.Module):
    """Critic network, employes two q-functions."""
    def __init__(
        self, obs_shape, action_shape, hidden_dim, encoder_type,
        encoder_feature_dim, num_layers, num_filters
    ):
        super().__init__()


        self.encoder = make_encoder(
            encoder_type, obs_shape, encoder_feature_dim, num_layers,
            num_filters
        )

        self.Q1 = QFunction(
            self.encoder.feature_dim, action_shape[0], hidden_dim
        )
        self.Q2 = QFunction(
            self.encoder.feature_dim, action_shape[0], hidden_dim
        )

        self.outputs = dict()
        self.apply(weight_init)

    def forward(self, obs, action, detach_encoder=False):
        # detach_encoder allows to stop gradient propogation to encoder
        obs, _ , _ = self.encoder(obs, detach=detach_encoder)

        q1 = self.Q1(obs, action)
        q2 = self.Q2(obs, action)

        self.outputs['q1'] = q1
        self.outputs['q2'] = q2

        return q1, q2

    def log(self, L, step, log_freq=LOG_FREQ):
        if step % log_freq != 0:
            return

        self.encoder.log(L, step, log_freq)

        for k, v in self.outputs.items():
            L.log_histogram('train_critic/%s_hist' % k, v, step)

        for i in range(3):
            L.log_param('train_critic/q1_fc%d' % i, self.Q1.trunk[i * 2], step)
            L.log_param('train_critic/q2_fc%d' % i, self.Q2.trunk[i * 2], step)

class CURL(nn.Module):
    """
    CURL
    """

    def __init__(self, obs_shape, z_dim, a_dim, batch_size, critic, critic_target, output_type="continuous"):
        super(CURL, self).__init__()
        self.batch_size = batch_size

        self.encoder = critic.encoder

        self.encoder_target = critic_target.encoder 

        self.W = nn.Parameter(torch.rand(z_dim, z_dim))
        self.combine = nn.Linear(z_dim + a_dim, z_dim)
        self.output_type = output_type

    def encode(self, x, a=None, detach=False, ema=False):
        """
        Encoder: z_t = e(x_t)
        :param x: x_t, x y coordinates
        :return: z_t, value in r2
        """
        if ema:
            with torch.no_grad():
                z_out = self.encoder_target(x)[0]
                z_out = self.combine(torch.concat((z_out,a), dim=-1))
        else:
            z_out = self.encoder(x)[0]

        if detach:
            z_out = z_out.detach()
        return z_out

    def compute_logits(self, z_a, z_pos):
        """
        Uses logits trick for CURL:
        - compute (B,B) matrix z_a (W z_pos.T)
        - positives are all diagonal elements
        - negatives are all other elements
        - to compute loss use multiclass cross entropy with identity matrix for labels
        """
        Wz = torch.matmul(self.W, z_pos.T)  # (z_dim,B)
        logits = torch.matmul(z_a, Wz)  # (B,B)
        logits = logits - torch.max(logits, 1)[0][:, None]
        return logits
        
class SacAeAgent(object):
    """SAC+AE algorithm."""
    def __init__(
        self,
        obs_shape,
        action_shape,
        device,
        hidden_dim=256,
        discount=0.99,
        init_temperature=0.01,
        alpha_lr=1e-3,
        alpha_beta=0.9,
        actor_lr=1e-3,
        actor_beta=0.9,
        actor_log_std_min=-10,
        actor_log_std_max=2,
        actor_update_freq=2,
        critic_lr=1e-3,
        critic_beta=0.9,
        critic_tau=0.005,
        critic_target_update_freq=2,
        encoder_type='pixel',
        encoder_feature_dim=50,
        encoder_lr=1e-3,
        encoder_tau=0.005,
        decoder_type='pixel',
        decoder_lr=1e-3,
        decoder_update_freq=1,
        decoder_latent_lambda=0.0,
        decoder_weight_lambda=0.0,
        num_layers=4,
        num_filters=32
    ):
        self.device = device
        self.discount = discount
        self.critic_tau = critic_tau
        self.encoder_tau = encoder_tau
        self.actor_update_freq = actor_update_freq
        self.critic_target_update_freq = critic_target_update_freq
        self.decoder_update_freq = decoder_update_freq
        self.decoder_latent_lambda = decoder_latent_lambda
        
        self.transition_model = TransitionModel(
            encoder_feature_dim, 
            hidden_dim, 
            action_shape[0], 
            encoder_feature_dim).to(device)

        self.actor = Actor(
            obs_shape, action_shape, hidden_dim, encoder_type,
            encoder_feature_dim, actor_log_std_min, actor_log_std_max,
            num_layers, num_filters
        ).to(device)

        self.critic = Critic(
            obs_shape, action_shape, hidden_dim, encoder_type,
            encoder_feature_dim, num_layers, num_filters
        ).to(device)

        self.critic_target = Critic(
            obs_shape, action_shape, hidden_dim, encoder_type,
            encoder_feature_dim, num_layers, num_filters
        ).to(device)

        self.critic_target.load_state_dict(self.critic.state_dict())

        # tie encoders between actor and critic
        self.actor.encoder.copy_conv_weights_from(self.critic.encoder)

        self.log_alpha = torch.tensor(np.log(init_temperature)).to(device)
        self.log_alpha.requires_grad = True
        # set target entropy to -|A|
        self.target_entropy = -np.prod(action_shape)

        self.CURL = CURL(obs_shape, encoder_feature_dim, action_shape[0],
                         obs_shape[0], self.critic,self.critic_target, output_type='continuous').to(self.device)
        
        self.cross_entropy_loss = nn.CrossEntropyLoss()

        self.decoder = None
        if decoder_type != 'identity':
            # create decoder
            self.decoder = make_decoder(
                decoder_type, obs_shape, encoder_feature_dim, num_layers,
                num_filters
            ).to(device)
            self.decoder.apply(weight_init)

            # optimizer for critic encoder for reconstruction loss
            self.encoder_optimizer = torch.optim.Adam(
                self.critic.encoder.parameters(), lr=encoder_lr
            )

            # optimizer for decoder
            self.decoder_optimizer = torch.optim.Adam(
                self.decoder.parameters(),
                lr=decoder_lr,
                weight_decay=decoder_weight_lambda
            )

        # optimizers
        self.actor_optimizer = torch.optim.Adam(
            self.actor.parameters(), lr=actor_lr, betas=(actor_beta, 0.999)
        )

        self.critic_optimizer = torch.optim.Adam(
            self.critic.parameters(), lr=critic_lr, betas=(critic_beta, 0.999)
        )

        self.cpc_optimizer = torch.optim.Adam(
                self.CURL.parameters(), lr=encoder_lr
        )
        
        self.log_alpha_optimizer = torch.optim.Adam(
            [self.log_alpha], lr=alpha_lr, betas=(alpha_beta, 0.999)
        )

        self.train()
        self.critic_target.train()

    def train(self, training=True):
        self.training = training
        self.actor.train(training)
        self.critic.train(training)
        if self.decoder is not None:
            self.decoder.train(training)

    @property
    def alpha(self):
        return self.log_alpha.exp()

    def select_action(self, obs):
        with torch.no_grad():
            obs = torch.FloatTensor(obs).to(self.device)
            obs = obs.unsqueeze(0)
            mu, _, _, _ = self.actor(
                obs, compute_pi=False, compute_log_pi=False
            )
            return mu.cpu().data.numpy().flatten()

    def sample_action(self, obs):
        with torch.no_grad():
            obs = torch.FloatTensor(obs).to(self.device)
            obs = obs.unsqueeze(0)
            mu, pi, _, _ = self.actor(obs, compute_log_pi=False)
            return pi.cpu().data.numpy().flatten()

    def update_critic(self, obs, action, reward, next_obs, not_done, L, step):
        with torch.no_grad():
            _, policy_action, log_pi, _ = self.actor(next_obs)
            target_Q1, target_Q2 = self.critic_target(next_obs, policy_action)
            target_V = torch.min(target_Q1,
                                 target_Q2) - self.alpha.detach() * log_pi
            target_Q = reward + (not_done * self.discount * target_V)

        # get current Q estimates
        current_Q1, current_Q2 = self.critic(obs, action)
        critic_loss = F.mse_loss(current_Q1,
                                 target_Q) + F.mse_loss(current_Q2, target_Q)
        L.log('train_critic/loss', critic_loss, step)

        # Optimize the critic
        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        self.critic_optimizer.step()

        self.critic.log(L, step)

    def update_actor_and_alpha(self, obs, L, step):
        # detach encoder, so we don't update it with the actor loss
        _, pi, log_pi, log_std = self.actor(obs, detach_encoder=True)
        actor_Q1, actor_Q2 = self.critic(obs, pi, detach_encoder=True)

        actor_Q = torch.min(actor_Q1, actor_Q2)
        actor_loss = (self.alpha.detach() * log_pi - actor_Q).mean()

        L.log('train_actor/loss', actor_loss, step)
        L.log('train_actor/target_entropy', self.target_entropy, step)
        entropy = 0.5 * log_std.shape[1] * (1.0 + np.log(2 * np.pi)
                                            ) + log_std.sum(dim=-1)
        L.log('train_actor/entropy', entropy.mean(), step)

        # optimize the actor
        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()

        self.actor.log(L, step)

        self.log_alpha_optimizer.zero_grad()
        alpha_loss = (self.alpha *
                      (-log_pi - self.target_entropy).detach()).mean()
        L.log('train_alpha/loss', alpha_loss, step)
        L.log('train_alpha/value', self.alpha, step)
        alpha_loss.backward()
        self.log_alpha_optimizer.step()

    def update_decoder(self, last_obs, last_action, last_reward, curr_obs, last_not_done, action, reward, next_obs, not_done, target_obs, L, step):
        h_curr, mu_h_curr, std_h_curr = self.critic.encoder(curr_obs)

        with torch.no_grad():
            h_last, _, _ = self.critic.encoder(last_obs)
            self.transition_model.init_states(last_obs.shape[0], self.device)
            curr_state = self.transition_model.transition_step(h_last, last_action, self.transition_model.prev_history, last_not_done)

            hist = curr_state["history"]
            next_state = self.transition_model.transition_step(h_curr, action, hist, not_done)

            next_state_mu = next_state["mean"]
            next_state_sigma = next_state["std"]
            next_state_sample = next_state["sample"]
            pred_dist = torch.distributions.Normal(next_state_mu, next_state_sigma)
        
        h, mu_h_next, logstd_h_next = self.critic.encoder(next_obs)
        std_h_next = torch.exp(logstd_h_next)
        enc_dist = torch.distributions.Normal(mu_h_next, std_h_next)
        enc_loss = torch.mean(torch.distributions.kl.kl_divergence(enc_dist,pred_dist)) * 0.1

        z_pos = self.CURL.encode(next_obs, action.detach(), ema=True)
        logits = self.CURL.compute_logits(h_curr, z_pos)
        labels = torch.arange(logits.shape[0]).long().to(self.device)
        lb_loss = self.cross_entropy_loss(logits, labels) * 0.1

        ub_loss = club_loss(h, mu_h_next, logstd_h_next, next_state_sample) * 0.1
    
        if target_obs.dim() == 4:
            # preprocess images to be in [-0.5, 0.5] range
            target_obs = utils.preprocess_obs(target_obs)

        rec_obs = self.decoder(h)
        rec_loss = F.mse_loss(target_obs, rec_obs)

        # add L2 penalty on latent representation
        # see https://arxiv.org/pdf/1903.12436.pdf
        latent_loss = (0.5 * h.pow(2).sum(1)).mean()

        loss = rec_loss + enc_loss + lb_loss + ub_loss #self.decoder_latent_lambda * latent_loss
        self.encoder_optimizer.zero_grad()
        self.decoder_optimizer.zero_grad()
        self.cpc_optimizer.zero_grad()
        loss.backward()
        
        self.encoder_optimizer.step()   
        self.decoder_optimizer.step()
        self.cpc_optimizer.step()
        L.log('train_ae/ae_loss', loss, step)
        L.log('train_ae/lb_loss', lb_loss, step)
        L.log('train_ae/ub_loss', ub_loss, step)
        L.log('train_ae/enc_loss', enc_loss, step)
        L.log('train_ae/dec_loss', rec_loss, step)

        self.decoder.log(L, step, log_freq=LOG_FREQ)

    def update(self, replay_buffer, L, step):
        last_obs, last_action, last_reward, curr_obs, last_not_done, action, reward, next_obs, not_done = replay_buffer.sample()
        #obs, action, reward, next_obs, not_done = replay_buffer.sample()

        L.log('train/batch_reward', last_reward.mean(), step)

        #self.update_critic(last_obs, last_action, last_reward, curr_obs, last_not_done, L, step)
        self.update_critic(curr_obs, action, reward, next_obs, not_done, L, step)

        if step % self.actor_update_freq == 0:
            #self.update_actor_and_alpha(last_obs, L, step)
            self.update_actor_and_alpha(curr_obs, L, step)

        if step % self.critic_target_update_freq == 0:
            utils.soft_update_params(
                self.critic.Q1, self.critic_target.Q1, self.critic_tau
            )
            utils.soft_update_params(
                self.critic.Q2, self.critic_target.Q2, self.critic_tau
            )
            utils.soft_update_params(
                self.critic.encoder, self.critic_target.encoder,
                self.encoder_tau
            )

        if self.decoder is not None and step % self.decoder_update_freq == 0:
            self.update_decoder(last_obs, last_action, last_reward, curr_obs, last_not_done, action, reward, next_obs, not_done, next_obs, L, step)

    def save(self, model_dir, step):
        torch.save(
            self.actor.state_dict(), '%s/actor_%s.pt' % (model_dir, step)
        )
        torch.save(
            self.critic.state_dict(), '%s/critic_%s.pt' % (model_dir, step)
        )
        if self.decoder is not None:
            torch.save(
                self.decoder.state_dict(),
                '%s/decoder_%s.pt' % (model_dir, step)
            )

    def load(self, model_dir, step):
        self.actor.load_state_dict(
            torch.load('%s/actor_%s.pt' % (model_dir, step))
        )
        self.critic.load_state_dict(
            torch.load('%s/critic_%s.pt' % (model_dir, step))
        )
        if self.decoder is not None:
            self.decoder.load_state_dict(
                torch.load('%s/decoder_%s.pt' % (model_dir, step))
            )