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from .sac_networks import Actor, SoftQNetwork, ResNETBackbone, SemanticBackbone,SemanticBackboneOC
import time
import torch
import torch.nn.functional as F
import torch.optim as optim
from tensordict import TensorDict
from torchrl.data import TensorDictReplayBuffer,LazyMemmapStorage
from torchrl.data.replay_buffers.samplers import SamplerWithoutReplacement
class SAC:
def __init__(self,
env,
args,
writer, critic_only_backbone=False):
self.critic_only_backbone = critic_only_backbone
self.env = env #train env
self.device = args.device
self.writer = writer
self.args = args
#networks
if self.env.use_txt_features=="embedded":
self.backbone = SemanticBackbone().to(self.device)
elif self.env.use_txt_features=="one_hot":
self.backbone = SemanticBackboneOC().to(self.device)
elif self.env.use_txt_features==False:
self.backbone = ResNETBackbone().to(self.device)
self.actor = Actor(env,self.backbone).to(self.device)
self.qf1 = SoftQNetwork(env,self.backbone).to(self.device)
self.qf2 = SoftQNetwork(env,self.backbone).to(self.device)
self.qf1_target = SoftQNetwork(env,self.backbone).to(self.device)
self.qf2_target = SoftQNetwork(env,self.backbone).to(self.device)
self.qf1_target.load_state_dict(self.qf1.state_dict())
self.qf2_target.load_state_dict(self.qf2.state_dict())
self.q_optimizer = optim.Adam(list(self.qf1.parameters()) + list(self.qf2.parameters()), lr=args.q_lr)
self.actor_optimizer = optim.Adam(list(self.actor.parameters()), lr=args.policy_lr)
#Training related
self.global_step = 0
self.start_time = None
# entropy
if args.autotune:
# self.target_entropy = -torch.prod(torch.Tensor(env.action_space._shape[1]).to(self.device)).item()
self.target_entropy = - env.action_space._shape[1]
self.log_alpha = torch.zeros(1, requires_grad=True, device=self.device)
self.alpha = self.log_alpha.exp().item()
self.a_optimizer = optim.Adam([self.log_alpha], lr=args.q_lr)
else:
self.alpha = args.alpha
#ReplayBuffer
self.rb = TensorDictReplayBuffer(
storage=LazyMemmapStorage(args.buffer_size,), sampler=SamplerWithoutReplacement()
)
def reset_env(self,):
self.state= self.env.reset()# observation tensor (B,N_Featuresx2)
def train(self,):
"""
perform one global step of training
"""
# # ALGO LOGIC: put action logic here
# runing_envs = self.env.sub_env_running # get running sub envs (images to be enhanced)
# # if len(runing_envs)<self.env.batch_size:
# # print('d',self.state,runing_envs)
# # print(self.state.shape,runing_envs.shape)
# # batch_obs= torch.index_select(self.state,0,runing_envs).to(self.device)
batch_obs = self.state.to(self.device)
if self.global_step < self.args.learning_starts:
actions = self.env.action_space.sample(batch_obs.shape[0]).to(self.device)
else:
actions, _, _ = self.actor.get_action(**batch_obs)
actions = actions.detach()
next_batch_obs, rewards, dones = self.env.step(actions)
batch_transition = TensorDict(
{
"observations":batch_obs.clone(),
"next_observations":next_batch_obs.clone(),
"actions":actions.clone(),
"rewards":rewards.clone(),
"dones":dones.clone(),
},
batch_size = [batch_obs.shape[0]],
)
self.rb.extend(batch_transition)
self.update()
# runing_envs = self.env.sub_env_running
# self.state = torch.index_select(next_batch_obs,0,runing_envs).to(self.device)
return rewards,dones
def act_eval(self,obs):
self.backbone.eval().requires_grad_(False)
self.qf1.eval().requires_grad_(False)
self.qf2.eval().requires_grad_(False)
self.actor.eval().requires_grad_(False)
with torch.no_grad():
actions = self.actor.get_action(**obs.to(self.device))
self.backbone.train().requires_grad_(True)
self.qf1.train().requires_grad_(True)
self.qf2.train().requires_grad_(True)
self.actor.train().requires_grad_(True)
return actions
def update(self,):
# ALGO LOGIC: training.
if self.global_step > self.args.learning_starts:
data = self.rb.sample(self.args.batch_size).to(self.device)
with torch.no_grad():
if self.args.gamma!=0:
next_state_actions, next_state_log_pi, _ = self.actor.get_action(**data["next_observations"])
qf1_next_target = self.qf1_target(**data["next_observations"], actions=next_state_actions)
qf2_next_target = self.qf2_target(**data["next_observations"], actions=next_state_actions)
min_qf_next_target = torch.min(qf1_next_target, qf2_next_target) - self.alpha * next_state_log_pi
next_q_value = data["rewards"].flatten() + (1 - data["dones"].to(torch.float32).flatten()) * self.args.gamma * (min_qf_next_target).view(-1)
else:
next_q_value = data["rewards"].flatten()
qf1_a_values = self.qf1(**data["observations"], actions = data["actions"]).view(-1)
qf2_a_values = self.qf2(**data["observations"], actions = data["actions"]).view(-1)
qf1_loss = F.mse_loss(qf1_a_values, next_q_value)
qf2_loss = F.mse_loss(qf2_a_values, next_q_value)
qf_loss = qf1_loss + qf2_loss
# optimize the model
self.q_optimizer.zero_grad()
qf_loss.backward()
self.q_optimizer.step()
if self.global_step % self.args.policy_frequency == 0: # TD 3 Delayed update support
if self.critic_only_backbone:
self.backbone.eval().requires_grad_(False)
for _ in range(
self.args.policy_frequency
): # compensate for the delay by doing 'actor_update_interval' instead of 1
pi, log_pi, _ = self.actor.get_action(**data["observations"])
qf1_pi = self.qf1(**data["observations"], actions=pi)
qf2_pi = self.qf2(**data["observations"], actions=pi)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
actor_loss = ((self.alpha * log_pi) - min_qf_pi).mean()
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
if self.args.autotune:
# with torch.no_grad():
# _, log_pi, _ = self.actor.get_action(data["observations"])
alpha_loss = (-self.log_alpha.exp() * (log_pi + self.target_entropy).detach()).mean()
self.a_optimizer.zero_grad()
alpha_loss.backward()
self.a_optimizer.step()
self.alpha = self.log_alpha.exp().item()
if self.critic_only_backbone:
self.backbone.train().requires_grad_(True)
# update the target networks
if self.args.gamma!=0:
if self.global_step % self.args.target_network_frequency == 0:
for param, target_param in zip(self.qf1.parameters(), self.qf1_target.parameters()):
target_param.data.copy_(self.args.tau * param.data + (1 - self.args.tau) * target_param.data)
for param, target_param in zip(self.qf2.parameters(), self.qf2_target.parameters()):
target_param.data.copy_(self.args.tau * param.data + (1 - self.args.tau) * target_param.data)
if self.global_step % 100 == 0:
self.writer.add_scalar("losses/qf1_values", qf1_a_values.mean().item(), self.global_step)
self.writer.add_scalar("losses/qf2_values", qf2_a_values.mean().item(), self.global_step)
self.writer.add_scalar("losses/qf1_loss", qf1_loss.item(), self.global_step)
self.writer.add_scalar("losses/qf2_loss", qf2_loss.item(), self.global_step)
self.writer.add_scalar("losses/qf_loss", qf_loss.item() / 2.0, self.global_step)
self.writer.add_scalar("losses/actor_loss", actor_loss.item(), self.global_step)
self.writer.add_scalar("losses/alpha", self.alpha, self.global_step)
# print("SPS:", int(self.global_step / (time.time() - self.start_time)))
self.writer.add_scalar("charts/SPS", int(self.global_step / (time.time() - self.start_time)), self.global_step)
if self.args.autotune:
self.writer.add_scalar("losses/alpha_loss", alpha_loss.item(), self.global_step)
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