364 lines
14 KiB
Python
364 lines
14 KiB
Python
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# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import utils
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from sac_ae import Actor, Critic, weight_init, LOG_FREQ
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from transition_model import make_transition_model
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from decoder import make_decoder
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class BaselineAgent(object):
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"""Baseline algorithm with transition model and various decoder types."""
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def __init__(
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self,
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obs_shape,
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action_shape,
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device,
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hidden_dim=256,
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discount=0.99,
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init_temperature=0.01,
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alpha_lr=1e-3,
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alpha_beta=0.9,
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actor_lr=1e-3,
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actor_beta=0.9,
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actor_log_std_min=-10,
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actor_log_std_max=2,
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actor_update_freq=2,
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critic_lr=1e-3,
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critic_beta=0.9,
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critic_tau=0.005,
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critic_target_update_freq=2,
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encoder_type='pixel',
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encoder_stride=2,
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encoder_feature_dim=50,
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encoder_lr=1e-3,
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encoder_tau=0.005,
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decoder_type='pixel',
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decoder_lr=1e-3,
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decoder_update_freq=1,
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decoder_weight_lambda=0.0,
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transition_model_type='deterministic',
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num_layers=4,
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num_filters=32
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):
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self.device = device
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self.discount = discount
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self.critic_tau = critic_tau
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self.encoder_tau = encoder_tau
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self.actor_update_freq = actor_update_freq
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self.critic_target_update_freq = critic_target_update_freq
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self.decoder_update_freq = decoder_update_freq
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self.decoder_type = decoder_type
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self.hinge = 1.
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self.sigma = 0.5
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self.actor = Actor(
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obs_shape, action_shape, hidden_dim, encoder_type,
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encoder_feature_dim, actor_log_std_min, actor_log_std_max,
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num_layers, num_filters, encoder_stride
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).to(device)
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self.critic = Critic(
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obs_shape, action_shape, hidden_dim, encoder_type,
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encoder_feature_dim, num_layers, num_filters, encoder_stride
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).to(device)
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self.critic_target = Critic(
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obs_shape, action_shape, hidden_dim, encoder_type,
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encoder_feature_dim, num_layers, num_filters, encoder_stride
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).to(device)
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self.critic_target.load_state_dict(self.critic.state_dict())
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self.transition_model = make_transition_model(
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transition_model_type, encoder_feature_dim, action_shape
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).to(device)
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# optimizer for decoder
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self.decoder_optimizer = torch.optim.Adam(
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self.transition_model.parameters(),
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lr=decoder_lr,
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weight_decay=decoder_weight_lambda
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)
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# tie encoders between actor and critic
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self.actor.encoder.copy_conv_weights_from(self.critic.encoder)
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self.log_alpha = torch.tensor(np.log(init_temperature)).to(device)
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self.log_alpha.requires_grad = True
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# set target entropy to -|A|
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self.target_entropy = -np.prod(action_shape)
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self.decoder = None
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encoder_params = list(self.critic.encoder.parameters()) + list(self.transition_model.parameters())
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if decoder_type == 'pixel':
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# create decoder
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self.decoder = make_decoder(
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decoder_type, obs_shape, encoder_feature_dim, num_layers,
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num_filters
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).to(device)
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self.decoder.apply(weight_init)
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elif decoder_type == 'inverse':
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self.inverse_model = nn.Sequential(
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nn.Linear(encoder_feature_dim * 2, 512),
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nn.LayerNorm(512),
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nn.ReLU(),
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nn.Linear(512, action_shape[0])).to(device)
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encoder_params += list(self.inverse_model.parameters())
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if decoder_type != 'identity':
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# optimizer for critic encoder for reconstruction loss
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self.encoder_optimizer = torch.optim.Adam(encoder_params, lr=encoder_lr)
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if decoder_type == 'pixel': # optimizer for decoder
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self.decoder_optimizer = torch.optim.Adam(
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self.decoder.parameters(),
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lr=decoder_lr,
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weight_decay=decoder_weight_lambda
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)
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# optimizer for critic encoder for reconstruction loss
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self.encoder_optimizer = torch.optim.Adam(
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self.critic.encoder.parameters(), lr=encoder_lr
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)
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# optimizers
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self.actor_optimizer = torch.optim.Adam(
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self.actor.parameters(), lr=actor_lr, betas=(actor_beta, 0.999)
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)
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self.critic_optimizer = torch.optim.Adam(
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self.critic.parameters(), lr=critic_lr, betas=(critic_beta, 0.999)
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)
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self.log_alpha_optimizer = torch.optim.Adam(
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[self.log_alpha], lr=alpha_lr, betas=(alpha_beta, 0.999)
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)
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self.train()
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self.critic_target.train()
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def energy(self, state, action, next_state, no_trans=False):
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"""Energy function based on normalized squared L2 norm."""
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norm = 0.5 / (self.sigma**2)
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if no_trans:
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diff = state - next_state
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normalization = 0.
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else:
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pred_trans_mu, pred_trans_sigma = self.transition_model(torch.cat([state, action], dim=1))
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if pred_trans_sigma is None:
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pred_trans_sigma = torch.Tensor([1.]).to(self.device)
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if isinstance(pred_trans_mu, list): # i.e. comes from an ensemble
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raise NotImplementedError # TODO: handle the additional ensemble dimension (0) in this case
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diff = (state + pred_trans_mu - next_state) / pred_trans_sigma
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normalization = torch.log(pred_trans_sigma)
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return norm * (diff.pow(2) + normalization).sum(1)
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def contrastive_loss(self, state, action, next_state):
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# Sample negative state across episodes at random
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batch_size = state.size(0)
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perm = np.random.permutation(batch_size)
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neg_state = state[perm]
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self.pos_loss = self.energy(state, action, next_state)
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zeros = torch.zeros_like(self.pos_loss)
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self.pos_loss = self.pos_loss.mean()
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self.neg_loss = torch.max(
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zeros, self.hinge - self.energy(
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state, action, neg_state, no_trans=True)).mean()
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loss = self.pos_loss + self.neg_loss
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return loss
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def train(self, training=True):
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self.training = training
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self.actor.train(training)
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self.critic.train(training)
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if self.decoder is not None:
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self.decoder.train(training)
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@property
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def alpha(self):
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return self.log_alpha.exp()
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def select_action(self, obs):
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with torch.no_grad():
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obs = torch.FloatTensor(obs).to(self.device)
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obs = obs.unsqueeze(0)
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mu, _, _, _ = self.actor(
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obs, compute_pi=False, compute_log_pi=False
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)
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return mu.cpu().data.numpy().flatten()
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def sample_action(self, obs):
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with torch.no_grad():
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obs = torch.FloatTensor(obs).to(self.device)
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obs = obs.unsqueeze(0)
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mu, pi, _, _ = self.actor(obs, compute_log_pi=False)
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return pi.cpu().data.numpy().flatten()
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def update_critic(self, obs, action, reward, next_obs, not_done, L, step):
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with torch.no_grad():
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_, policy_action, log_pi, _ = self.actor(next_obs)
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target_Q1, target_Q2 = self.critic_target(next_obs, policy_action)
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target_V = torch.min(target_Q1,
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target_Q2) - self.alpha.detach() * log_pi
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target_Q = reward + (not_done * self.discount * target_V)
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# get current Q estimates
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current_Q1, current_Q2 = self.critic(obs, action, detach_encoder=False)
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critic_loss = F.mse_loss(current_Q1,
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target_Q) + F.mse_loss(current_Q2, target_Q)
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L.log('train_critic/loss', critic_loss, step)
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# Optimize the critic
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self.critic_optimizer.zero_grad()
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critic_loss.backward()
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self.critic_optimizer.step()
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self.critic.log(L, step)
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def update_actor_and_alpha(self, obs, L, step):
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# detach encoder, so we don't update it with the actor loss
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_, pi, log_pi, log_std = self.actor(obs, detach_encoder=True)
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actor_Q1, actor_Q2 = self.critic(obs, pi, detach_encoder=True)
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actor_Q = torch.min(actor_Q1, actor_Q2)
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actor_loss = (self.alpha.detach() * log_pi - actor_Q).mean()
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L.log('train_actor/loss', actor_loss, step)
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L.log('train_actor/target_entropy', self.target_entropy, step)
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entropy = 0.5 * log_std.shape[1] * (1.0 + np.log(2 * np.pi)
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) + log_std.sum(dim=-1)
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L.log('train_actor/entropy', entropy.mean(), step)
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# optimize the actor
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self.actor_optimizer.zero_grad()
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actor_loss.backward()
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self.actor_optimizer.step()
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self.actor.log(L, step)
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self.log_alpha_optimizer.zero_grad()
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alpha_loss = (self.alpha *
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(-log_pi - self.target_entropy).detach()).mean()
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L.log('train_alpha/loss', alpha_loss, step)
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L.log('train_alpha/value', self.alpha, step)
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alpha_loss.backward()
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self.log_alpha_optimizer.step()
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def update_decoder(self, obs, action, target_obs, L, step): # uses transition model
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# image might be stacked, just grab the first 3 (rgb)!
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assert target_obs.dim() == 4
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target_obs = target_obs[:, :3, :, :]
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h = self.critic.encoder(obs)
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next_h = self.transition_model.sample_prediction(torch.cat([h, action], dim=1))
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if target_obs.dim() == 4:
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# preprocess images to be in [-0.5, 0.5] range
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target_obs = utils.preprocess_obs(target_obs)
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rec_obs = self.decoder(next_h)
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loss = F.mse_loss(target_obs, rec_obs)
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self.encoder_optimizer.zero_grad()
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self.decoder_optimizer.zero_grad()
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loss.backward()
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self.encoder_optimizer.step()
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self.decoder_optimizer.step()
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L.log('train_ae/ae_loss', loss, step)
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self.decoder.log(L, step, log_freq=LOG_FREQ)
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def update_contrastive(self, obs, action, next_obs, L, step):
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latent = self.critic.encoder(obs)
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next_latent = self.critic.encoder(next_obs)
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loss = self.contrastive_loss(latent, action, next_latent)
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self.encoder_optimizer.zero_grad()
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self.decoder_optimizer.zero_grad()
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loss.backward()
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self.encoder_optimizer.step()
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self.decoder_optimizer.step()
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L.log('train_ae/contrastive_loss', loss, step)
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def update_inverse(self, obs, action, next_obs, L, step):
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non_final_mask = torch.tensor(tuple(map(lambda s: not (s == 0).all(), next_obs)), device=self.device).long() # hack
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latent = self.critic.encoder(obs[non_final_mask])
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next_latent = self.critic.encoder(next_obs[non_final_mask].to(self.device).float())
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# pred_next_latent = self.transition_model(torch.cat([latent, action], dim=1))
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# fpred_action = self.inverse_model(latent, pred_next_latent)
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pred_action = self.inverse_model(torch.cat([latent, next_latent], dim=1))
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loss = F.mse_loss(pred_action, action[non_final_mask]) # + F.mse_loss(fpred_action, action)
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self.encoder_optimizer.zero_grad()
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loss.backward()
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self.encoder_optimizer.step()
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L.log('train_ae/inverse_loss', loss, step)
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def update(self, replay_buffer, L, step):
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if self.decoder_type == 'inverse':
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obs, action, reward, next_obs, not_done, k_obs = replay_buffer.sample(k=True)
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else:
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obs, action, _, reward, next_obs, not_done = replay_buffer.sample()
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L.log('train/batch_reward', reward.mean(), step)
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self.update_critic(obs, action, reward, next_obs, not_done, L, step)
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if step % self.actor_update_freq == 0:
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self.update_actor_and_alpha(obs, L, step)
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if step % self.critic_target_update_freq == 0:
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utils.soft_update_params(
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self.critic.Q1, self.critic_target.Q1, self.critic_tau
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)
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utils.soft_update_params(
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self.critic.Q2, self.critic_target.Q2, self.critic_tau
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)
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utils.soft_update_params(
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self.critic.encoder, self.critic_target.encoder,
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self.encoder_tau
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)
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if self.decoder is not None and step % self.decoder_update_freq == 0: # decoder_type is pixel
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self.update_decoder(obs, action, next_obs, L, step)
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if self.decoder_type == 'contrastive':
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self.update_contrastive(obs, action, next_obs, L, step)
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elif self.decoder_type == 'inverse':
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self.update_inverse(obs, action, k_obs, L, step)
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def save(self, model_dir, step):
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torch.save(
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self.actor.state_dict(), '%s/actor_%s.pt' % (model_dir, step)
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)
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torch.save(
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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))
|
||
|
|
)
|