repo_description: CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
repo_topics: contrastive-learning contrastive-loss contrastive-predictive-coding curl deep-learning deep-learning-algorithms deep-neural-networks deep-q-learning deep-q-network deep-reinforcement-learning deep-rl deeplearning deeplearning-ai gpu model-free-rl off-policy reinforcement-agents reinforcement-learning reinforcement-learning-algorithms sac
code: 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
  
  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, output_logits=True
          )
  
          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, output_logits=True
          )
  
          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, 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.output_type = output_type
  
      def encode(self, x, 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)
          else:
              z_out = self.encoder(x)
  
          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 CurlSacAgent(object):
      """CURL representation learning with SAC."""
      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,
          num_layers=4,
          num_filters=32,
          cpc_update_freq=1,
          log_interval=100,
          detach_encoder=False,
          curl_latent_dim=128
      ):
          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.cpc_update_freq = cpc_update_freq
          self.log_interval = log_interval
          self.image_size = obs_shape[-1]
          self.curl_latent_dim = curl_latent_dim
          self.detach_encoder = detach_encoder
          self.encoder_type = encoder_type
  
          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, and CURL 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)
          
          # 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.log_alpha_optimizer = torch.optim.Adam(
              [self.log_alpha], lr=alpha_lr, betas=(alpha_beta, 0.999)
          )
  
          if self.encoder_type == 'pixel':
              # create CURL encoder (the 128 batch size is probably unnecessary)
              self.CURL = CURL(obs_shape, encoder_feature_dim,
                          self.curl_latent_dim, self.critic,self.critic_target, output_type='continuous').to(self.device)
  
              # optimizer for critic encoder for reconstruction loss
              self.encoder_optimizer = torch.optim.Adam(
                  self.critic.encoder.parameters(), lr=encoder_lr
              )
  
              self.cpc_optimizer = torch.optim.Adam(
                  self.CURL.parameters(), lr=encoder_lr
              )
          self.cross_entropy_loss = nn.CrossEntropyLoss()
  
          self.train()
          self.critic_target.train()
  
      def train(self, training=True):
          self.training = training
          self.actor.train(training)
          self.critic.train(training)
          if self.encoder_type == 'pixel':
              self.CURL.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):
          if obs.shape[-1] != self.image_size:
              obs = utils.center_crop_image(obs, self.image_size)
   
          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, detach_encoder=self.detach_encoder)
          critic_loss = F.mse_loss(current_Q1,
                                   target_Q) + F.mse_loss(current_Q2, target_Q)
          if step % self.log_interval == 0:
              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()
  
          if step % self.log_interval == 0:
              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)
          if step % self.log_interval == 0:                                    
              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()
          if step % self.log_interval == 0:
              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_cpc(self, obs_anchor, obs_pos, cpc_kwargs, L, step):
          
          z_a = self.CURL.encode(obs_anchor)
          z_pos = self.CURL.encode(obs_pos, ema=True)
          
          logits = self.CURL.compute_logits(z_a, z_pos)
          labels = torch.arange(logits.shape[0]).long().to(self.device)
          loss = self.cross_entropy_loss(logits, labels)
          
          self.encoder_optimizer.zero_grad()
          self.cpc_optimizer.zero_grad()
          loss.backward()
  
          self.encoder_optimizer.step()
          self.cpc_optimizer.step()
          if step % self.log_interval == 0:
              L.log('train/curl_loss', loss, step)
  
  
      def update(self, replay_buffer, L, step):
          if self.encoder_type == 'pixel':
              obs, action, reward, next_obs, not_done, cpc_kwargs = replay_buffer.sample_cpc()
          else:
              obs, action, reward, next_obs, not_done = replay_buffer.sample_proprio()
      
          if step % self.log_interval == 0:
              L.log('train/batch_reward', reward.mean(), step)
  
          self.update_critic(obs, action, reward, next_obs, not_done, L, step)
  
          if step % self.actor_update_freq == 0:
              self.update_actor_and_alpha(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 step % self.cpc_update_freq == 0 and self.encoder_type == 'pixel':
              obs_anchor, obs_pos = cpc_kwargs["obs_anchor"], cpc_kwargs["obs_pos"]
              self.update_cpc(obs_anchor, obs_pos,cpc_kwargs, 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)
          )
  
      def save_curl(self, model_dir, step):
          torch.save(
              self.CURL.state_dict(), '%s/curl_%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))
          )
   