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drqv2.py
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drqv2.py
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hydra
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import utils
class RandomShiftsAug(nn.Module):
def __init__(self, pad):
super().__init__()
self.pad = pad
def forward(self, x):
n, c, h, w = x.size()
assert h == w
padding = tuple([self.pad] * 4)
x = F.pad(x, padding, 'replicate')
eps = 1.0 / (h + 2 * self.pad)
arange = torch.linspace(-1.0 + eps,
1.0 - eps,
h + 2 * self.pad,
device=x.device,
dtype=x.dtype)[:h]
arange = arange.unsqueeze(0).repeat(h, 1).unsqueeze(2)
base_grid = torch.cat([arange, arange.transpose(1, 0)], dim=2)
base_grid = base_grid.unsqueeze(0).repeat(n, 1, 1, 1)
shift = torch.randint(0,
2 * self.pad + 1,
size=(n, 1, 1, 2),
device=x.device,
dtype=x.dtype)
shift *= 2.0 / (h + 2 * self.pad)
grid = base_grid + shift
return F.grid_sample(x,
grid,
padding_mode='zeros',
align_corners=False)
class Encoder(nn.Module):
def __init__(self, obs_shape):
super().__init__()
assert len(obs_shape) == 3
self.repr_dim = 32 * 35 * 35
self.convnet = nn.Sequential(nn.Conv2d(obs_shape[0], 32, 3, stride=2),
nn.ReLU(), nn.Conv2d(32, 32, 3, stride=1),
nn.ReLU(), nn.Conv2d(32, 32, 3, stride=1),
nn.ReLU(), nn.Conv2d(32, 32, 3, stride=1),
nn.ReLU())
self.apply(utils.weight_init)
def forward(self, obs):
obs = obs / 255.0 - 0.5
h = self.convnet(obs)
h = h.view(h.shape[0], -1)
return h
class Actor(nn.Module):
def __init__(self, repr_dim, action_shape, feature_dim, hidden_dim):
super().__init__()
self.trunk = nn.Sequential(nn.Linear(repr_dim, feature_dim),
nn.LayerNorm(feature_dim), nn.Tanh())
self.policy = nn.Sequential(nn.Linear(feature_dim, hidden_dim),
nn.ReLU(inplace=True),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(inplace=True),
nn.Linear(hidden_dim, action_shape[0]))
self.apply(utils.weight_init)
def forward(self, obs, std):
h = self.trunk(obs)
mu = self.policy(h)
mu = torch.tanh(mu)
std = torch.ones_like(mu) * std
dist = utils.TruncatedNormal(mu, std)
return dist
class Critic(nn.Module):
def __init__(self, repr_dim, action_shape, feature_dim, hidden_dim):
super().__init__()
self.trunk = nn.Sequential(nn.Linear(repr_dim, feature_dim),
nn.LayerNorm(feature_dim), nn.Tanh())
self.Q1 = nn.Sequential(
nn.Linear(feature_dim + action_shape[0], hidden_dim),
nn.ReLU(inplace=True), nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(inplace=True), nn.Linear(hidden_dim, 1))
self.Q2 = nn.Sequential(
nn.Linear(feature_dim + action_shape[0], hidden_dim),
nn.ReLU(inplace=True), nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(inplace=True), nn.Linear(hidden_dim, 1))
self.apply(utils.weight_init)
def forward(self, obs, action):
h = self.trunk(obs)
h_action = torch.cat([h, action], dim=-1)
q1 = self.Q1(h_action)
q2 = self.Q2(h_action)
return q1, q2
class DrQV2Agent:
def __init__(self, obs_shape, action_shape, device, lr, feature_dim,
hidden_dim, critic_target_tau, num_expl_steps,
update_every_steps, stddev_schedule, stddev_clip, use_tb):
self.device = device
self.critic_target_tau = critic_target_tau
self.update_every_steps = update_every_steps
self.use_tb = use_tb
self.num_expl_steps = num_expl_steps
self.stddev_schedule = stddev_schedule
self.stddev_clip = stddev_clip
# models
self.encoder = Encoder(obs_shape).to(device)
self.actor = Actor(self.encoder.repr_dim, action_shape, feature_dim,
hidden_dim).to(device)
self.critic = Critic(self.encoder.repr_dim, action_shape, feature_dim,
hidden_dim).to(device)
self.critic_target = Critic(self.encoder.repr_dim, action_shape,
feature_dim, hidden_dim).to(device)
self.critic_target.load_state_dict(self.critic.state_dict())
# optimizers
self.encoder_opt = torch.optim.Adam(self.encoder.parameters(), lr=lr)
self.actor_opt = torch.optim.Adam(self.actor.parameters(), lr=lr)
self.critic_opt = torch.optim.Adam(self.critic.parameters(), lr=lr)
# data augmentation
self.aug = RandomShiftsAug(pad=4)
self.train()
self.critic_target.train()
def train(self, training=True):
self.training = training
self.encoder.train(training)
self.actor.train(training)
self.critic.train(training)
def act(self, obs, step, eval_mode):
obs = torch.as_tensor(obs, device=self.device)
obs = self.encoder(obs.unsqueeze(0))
stddev = utils.schedule(self.stddev_schedule, step)
dist = self.actor(obs, stddev)
if eval_mode:
action = dist.mean
else:
action = dist.sample(clip=None)
if step < self.num_expl_steps:
action.uniform_(-1.0, 1.0)
return action.cpu().numpy()[0]
def update_critic(self, obs, action, reward, discount, next_obs, step):
metrics = dict()
with torch.no_grad():
stddev = utils.schedule(self.stddev_schedule, step)
dist = self.actor(next_obs, stddev)
next_action = dist.sample(clip=self.stddev_clip)
target_Q1, target_Q2 = self.critic_target(next_obs, next_action)
target_V = torch.min(target_Q1, target_Q2)
target_Q = reward + (discount * target_V)
Q1, Q2 = self.critic(obs, action)
critic_loss = F.mse_loss(Q1, target_Q) + F.mse_loss(Q2, target_Q)
if self.use_tb:
metrics['critic_target_q'] = target_Q.mean().item()
metrics['critic_q1'] = Q1.mean().item()
metrics['critic_q2'] = Q2.mean().item()
metrics['critic_loss'] = critic_loss.item()
# optimize encoder and critic
self.encoder_opt.zero_grad(set_to_none=True)
self.critic_opt.zero_grad(set_to_none=True)
critic_loss.backward()
self.critic_opt.step()
self.encoder_opt.step()
return metrics
def update_actor(self, obs, step):
metrics = dict()
stddev = utils.schedule(self.stddev_schedule, step)
dist = self.actor(obs, stddev)
action = dist.sample(clip=self.stddev_clip)
log_prob = dist.log_prob(action).sum(-1, keepdim=True)
Q1, Q2 = self.critic(obs, action)
Q = torch.min(Q1, Q2)
actor_loss = -Q.mean()
# optimize actor
self.actor_opt.zero_grad(set_to_none=True)
actor_loss.backward()
self.actor_opt.step()
if self.use_tb:
metrics['actor_loss'] = actor_loss.item()
metrics['actor_logprob'] = log_prob.mean().item()
metrics['actor_ent'] = dist.entropy().sum(dim=-1).mean().item()
return metrics
def update(self, replay_iter, step):
metrics = dict()
if step % self.update_every_steps != 0:
return metrics
batch = next(replay_iter)
obs, action, reward, discount, next_obs = utils.to_torch(
batch, self.device)
# augment
obs = self.aug(obs.float())
next_obs = self.aug(next_obs.float())
# encode
obs = self.encoder(obs)
with torch.no_grad():
next_obs = self.encoder(next_obs)
if self.use_tb:
metrics['batch_reward'] = reward.mean().item()
# update critic
metrics.update(
self.update_critic(obs, action, reward, discount, next_obs, step))
# update actor
metrics.update(self.update_actor(obs.detach(), step))
# update critic target
utils.soft_update_params(self.critic, self.critic_target,
self.critic_target_tau)
return metrics