mirror of
https://github.com/wassname/curl.git
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498 lines
16 KiB
Python
498 lines
16 KiB
Python
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 copy
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import math
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from pathlib import Path
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import utils
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from encoder import make_encoder
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LOG_FREQ = 10000
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def gaussian_logprob(noise, log_std):
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"""Compute Gaussian log probability."""
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residual = (-0.5 * noise.pow(2) - log_std).sum(-1, keepdim=True)
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return residual - 0.5 * np.log(2 * np.pi) * noise.size(-1)
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def squash(mu, pi, log_pi):
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"""Apply squashing function.
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See appendix C from https://arxiv.org/pdf/1812.05905.pdf.
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"""
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mu = torch.tanh(mu)
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if pi is not None:
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pi = torch.tanh(pi)
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if log_pi is not None:
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log_pi -= torch.log(F.relu(1 - pi.pow(2)) + 1e-6).sum(-1, keepdim=True)
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return mu, pi, log_pi
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def weight_init(m):
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"""Custom weight init for Conv2D and Linear layers."""
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if isinstance(m, nn.Linear):
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nn.init.orthogonal_(m.weight.data)
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m.bias.data.fill_(0.0)
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elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
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# delta-orthogonal init from https://arxiv.org/pdf/1806.05393.pdf
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assert m.weight.size(2) == m.weight.size(3)
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m.weight.data.fill_(0.0)
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m.bias.data.fill_(0.0)
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mid = m.weight.size(2) // 2
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gain = nn.init.calculate_gain('relu')
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nn.init.orthogonal_(m.weight.data[:, :, mid, mid], gain)
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class Actor(nn.Module):
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"""MLP actor network."""
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def __init__(
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self, obs_shape, action_shape, hidden_dim, encoder_type,
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encoder_feature_dim, log_std_min, log_std_max, num_layers, num_filters
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):
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super().__init__()
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self.encoder = make_encoder(
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encoder_type, obs_shape, encoder_feature_dim, num_layers,
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num_filters, output_logits=True
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)
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self.log_std_min = log_std_min
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self.log_std_max = log_std_max
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self.trunk = nn.Sequential(
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nn.Linear(self.encoder.feature_dim, hidden_dim), nn.ReLU(),
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nn.Linear(hidden_dim, hidden_dim), nn.ReLU(),
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nn.Linear(hidden_dim, 2 * action_shape[0])
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)
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self.outputs = dict()
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self.apply(weight_init)
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def forward(
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self, obs, compute_pi=True, compute_log_pi=True, detach_encoder=False
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):
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obs = self.encoder(obs, detach=detach_encoder)
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mu, log_std = self.trunk(obs).chunk(2, dim=-1)
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# constrain log_std inside [log_std_min, log_std_max]
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log_std = torch.tanh(log_std)
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log_std = self.log_std_min + 0.5 * (
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self.log_std_max - self.log_std_min
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) * (log_std + 1)
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self.outputs['mu'] = mu
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self.outputs['std'] = log_std.exp()
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if compute_pi:
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std = log_std.exp()
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noise = torch.randn_like(mu)
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pi = mu + noise * std
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else:
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pi = None
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entropy = None
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if compute_log_pi:
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log_pi = gaussian_logprob(noise, log_std)
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else:
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log_pi = None
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mu, pi, log_pi = squash(mu, pi, log_pi)
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return mu, pi, log_pi, log_std
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def log(self, L, step, log_freq=LOG_FREQ):
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if step % log_freq != 0:
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return
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for k, v in self.outputs.items():
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L.log_histogram('train_actor/%s_hist' % k, v, step)
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L.log_param('train_actor/fc1', self.trunk[0], step)
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L.log_param('train_actor/fc2', self.trunk[2], step)
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L.log_param('train_actor/fc3', self.trunk[4], step)
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class QFunction(nn.Module):
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"""MLP for q-function."""
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def __init__(self, obs_dim, action_dim, hidden_dim):
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super().__init__()
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self.trunk = nn.Sequential(
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nn.Linear(obs_dim + action_dim, hidden_dim), nn.ReLU(),
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nn.Linear(hidden_dim, hidden_dim), nn.ReLU(),
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nn.Linear(hidden_dim, 1)
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)
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def forward(self, obs, action):
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assert obs.size(0) == action.size(0)
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obs_action = torch.cat([obs, action], dim=1)
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return self.trunk(obs_action)
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class Critic(nn.Module):
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"""Critic network, employes two q-functions."""
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def __init__(
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self, obs_shape, action_shape, hidden_dim, encoder_type,
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encoder_feature_dim, num_layers, num_filters
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):
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super().__init__()
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self.encoder = make_encoder(
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encoder_type, obs_shape, encoder_feature_dim, num_layers,
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num_filters, output_logits=True
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)
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self.Q1 = QFunction(
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self.encoder.feature_dim, action_shape[0], hidden_dim
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)
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self.Q2 = QFunction(
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self.encoder.feature_dim, action_shape[0], hidden_dim
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)
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self.outputs = dict()
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self.apply(weight_init)
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def forward(self, obs, action, detach_encoder=False):
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# detach_encoder allows to stop gradient propogation to encoder
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obs = self.encoder(obs, detach=detach_encoder)
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q1 = self.Q1(obs, action)
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q2 = self.Q2(obs, action)
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self.outputs['q1'] = q1
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self.outputs['q2'] = q2
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return q1, q2
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def log(self, L, step, log_freq=LOG_FREQ):
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if step % log_freq != 0:
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return
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self.encoder.log(L, step, log_freq)
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for k, v in self.outputs.items():
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L.log_histogram('train_critic/%s_hist' % k, v, step)
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for i in range(3):
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L.log_param('train_critic/q1_fc%d' % i, self.Q1.trunk[i * 2], step)
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L.log_param('train_critic/q2_fc%d' % i, self.Q2.trunk[i * 2], step)
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class CURL(nn.Module):
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"""
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CURL
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"""
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def __init__(self, obs_shape, z_dim, batch_size, critic, critic_target, output_type="continuous"):
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super(CURL, self).__init__()
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self.batch_size = batch_size
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self.encoder = critic.encoder
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self.encoder_target = critic_target.encoder
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self.W = nn.Parameter(torch.rand(z_dim, z_dim))
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self.output_type = output_type
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def encode(self, x, detach=False, ema=False):
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"""
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Encoder: z_t = e(x_t)
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:param x: x_t, x y coordinates
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:return: z_t, value in r2
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"""
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if ema:
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with torch.no_grad():
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z_out = self.encoder_target(x, with_state=False)
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else:
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z_out = self.encoder(x, with_state=False)
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if detach:
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z_out = z_out.detach()
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return z_out
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def compute_logits(self, z_a, z_pos):
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"""
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Uses logits trick for CURL:
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- compute (B,B) matrix z_a (W z_pos.T)
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- positives are all diagonal elements
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- negatives are all other elements
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- to compute loss use multiclass cross entropy with identity matrix for labels
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"""
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Wz = torch.matmul(self.W, z_pos.T) # (z_dim,B)
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logits = torch.matmul(z_a, Wz) # (B,B)
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logits = logits - torch.max(logits, 1)[0][:, None]
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return logits
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class CurlSacAgent(object):
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"""CURL representation learning with SAC."""
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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='mixed',
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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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num_layers=4,
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num_filters=32,
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cpc_update_freq=1,
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log_interval=100,
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detach_encoder=False,
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curl_latent_dim=128
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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.cpc_update_freq = cpc_update_freq
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self.log_interval = log_interval
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self.image_size = obs_shape['img'][-1]
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self.curl_latent_dim = curl_latent_dim
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self.detach_encoder = detach_encoder
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self.encoder_type = encoder_type
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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
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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
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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
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).to(device)
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self.critic_target.load_state_dict(self.critic.state_dict())
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# tie encoders between actor and critic, and CURL 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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# 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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if self.encoder_type == 'mixed':
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# create CURL encoder (the 128 batch size is probably unnecessary)
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self.CURL = CURL(obs_shape, encoder_feature_dim,
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self.curl_latent_dim, self.critic,self.critic_target, output_type='continuous').to(self.device)
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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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self.cpc_optimizer = torch.optim.Adam(
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self.CURL.parameters(), lr=encoder_lr
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)
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self.cross_entropy_loss = nn.CrossEntropyLoss()
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self.train()
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self.critic_target.train()
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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.encoder_type == 'mixed':
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self.CURL.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['img'] = torch.FloatTensor(obs['img']).to(self.device).unsqueeze(0)
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obs['state'] = torch.FloatTensor(obs['state']).to(self.device).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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if obs['img'].shape[-1] != self.image_size:
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state, img = utils.split_obs(obs)
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img = utils.center_crop_image(img, self.image_size)
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obs = utils.combine_obs(state, img)
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with torch.no_grad():
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obs['img'] = torch.FloatTensor(obs['img']).to(self.device).unsqueeze(0)
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obs['state'] = torch.FloatTensor(obs['state']).to(self.device).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(
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obs, action, detach_encoder=self.detach_encoder)
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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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if step % self.log_interval == 0:
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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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if step % self.log_interval == 0:
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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] * \
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(1.0 + np.log(2 * np.pi)) + log_std.sum(dim=-1)
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if step % self.log_interval == 0:
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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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if step % self.log_interval == 0:
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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_cpc(self, obs_anchor, obs_pos, cpc_kwargs, L, step):
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z_a = self.CURL.encode(obs_anchor)
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z_pos = self.CURL.encode(obs_pos, ema=True)
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logits = self.CURL.compute_logits(z_a, z_pos)
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labels = torch.arange(logits.shape[0]).long().to(self.device)
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loss = self.cross_entropy_loss(logits, labels)
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self.encoder_optimizer.zero_grad()
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self.cpc_optimizer.zero_grad()
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loss.backward()
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self.encoder_optimizer.step()
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self.cpc_optimizer.step()
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if step % self.log_interval == 0:
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L.log('train/curl_loss', loss, step)
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def update(self, replay_buffer, L, step):
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if self.encoder_type == 'mixed':
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obs, action, reward, next_obs, not_done, cpc_kwargs = replay_buffer.sample_cpc()
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else:
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obs, action, reward, next_obs, not_done = replay_buffer.sample_proprio()
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if step % self.log_interval == 0:
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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 step % self.cpc_update_freq == 0 and self.encoder_type == 'mixed':
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obs_anchor, obs_pos = cpc_kwargs["obs_anchor"], cpc_kwargs["obs_pos"]
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self.update_cpc(obs_anchor, obs_pos,cpc_kwargs, 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)
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# )
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def save_curl(self, model_dir, step):
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torch.save(
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self.CURL.state_dict(), '%s/curl_%s.pt' % (model_dir, step)
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|
)
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|
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|
# def load(self, model_dir, step):
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|
# self.actor.load_state_dict(
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# torch.load('%s/actor_%s.pt' % (model_dir, step))
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|
# )
|
|
# self.critic.load_state_dict(
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|
# torch.load('%s/critic_%s.pt' % (model_dir, step))
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|
# )
|
|
|
|
def load_curl(self, model_dir, step=None):
|
|
if step is None:
|
|
steps = sorted([int(f.stem.split('_')[1]) for f in Path(model_dir).glob("curl_*.pt")])
|
|
step = steps[-1]
|
|
print(f'auto loading steps step {step}')
|
|
self.CURL.load_state_dict(
|
|
torch.load('%s/curl_%s.pt' % (model_dir, step))
|
|
)
|
|
|