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CAC_torch.py
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CAC_torch.py
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import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import random
from collections import deque
from torch_networks import AC_a_fc_network, AC_v_fc_network, CAC_a_fc_network
from helper_functions import SlidingMemory, PERMemory
import warnings
warnings.simplefilter("error", RuntimeWarning)
class CAC():
def __init__(self, state_dim, action_dim, mem_size, train_batch_size, gamma, actor_lr, critic_lr,
tau, action_low, action_high, if_PER = True):
self.mem_size, self.train_batch_size = mem_size, train_batch_size
self.gamma, self.actor_lr, self.critic_lr = gamma, actor_lr, critic_lr
self.global_step = 0
self.tau, self.if_PER= tau, if_PER
self.state_dim, self.action_dim = state_dim, action_dim
self.replay_mem = PERMemory(mem_size) if if_PER else SlidingMemory(mem_size)
#self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.device = 'cpu'
self.action_low, self.action_high = action_low, action_high
self.actor_policy_net = CAC_a_fc_network(state_dim, action_dim, action_low, action_high).to(self.device)
self.actor_target_net = CAC_a_fc_network(state_dim, action_dim, action_low, action_high).to(self.device)
self.critic_policy_net = AC_v_fc_network(state_dim).to(self.device)
self.critic_target_net = AC_v_fc_network(state_dim).to(self.device)
self.actor_optimizer = optim.Adam(self.actor_policy_net.parameters(), self.actor_lr)
self.critic_optimizer = optim.Adam(self.critic_policy_net.parameters(), self.critic_lr)
self.hard_update(self.actor_target_net, self.actor_policy_net)
self.hard_update(self.critic_target_net, self.critic_policy_net)
def soft_update(self, target, source, tau):
for target_param, param in zip(target.parameters(), source.parameters()):
target_param.data.copy_(target_param.data * (1.0 - tau) + param.data * tau)
def hard_update(self, target, source):
for target_param, param in zip(target.parameters(), source.parameters()):
target_param.data.copy_(param.data)
# training process
def train(self, pre_state, action, reward, next_state, if_end):
self.replay_mem.add(pre_state, action, reward, next_state, if_end)
if self.replay_mem.num() < self.mem_size:
return
# sample $self.train_batch_size$ samples from the replay memory, and use them to train
if not self.if_PER:
train_batch = self.replay_mem.sample(self.train_batch_size)
else:
train_batch, idx_batch, weight_batch = self.replay_mem.sample(self.train_batch_size)
weight_batch = torch.tensor(weight_batch, dtype = torch.float).unsqueeze(1)
# adjust dtype to suit the gym default dtype
pre_state_batch = torch.tensor([x[0] for x in train_batch], dtype=torch.float, device = self.device)
action_batch = torch.tensor([x[1] for x in train_batch], dtype = torch.float, device = self.device)
# view to make later computation happy
reward_batch = torch.tensor([x[2] for x in train_batch], dtype=torch.float, device = self.device).view(self.train_batch_size,1)
next_state_batch = torch.tensor([x[3] for x in train_batch], dtype=torch.float, device = self.device)
if_end = [x[4] for x in train_batch]
if_end = torch.tensor(np.array(if_end).astype(float),device = self.device, dtype=torch.float).view(self.train_batch_size,1)
# use the target_Q_network to get the target_Q_value
with torch.no_grad():
v_next_state = self.critic_target_net(next_state_batch).detach()
v_target = self.gamma * v_next_state * (1 - if_end) + reward_batch
v_pred = self.critic_policy_net(pre_state_batch)
if self.if_PER:
TD_error_batch = np.abs(v_target.numpy() - v_pred.detach().numpy())
self.replay_mem.update(idx_batch, TD_error_batch)
self.critic_optimizer.zero_grad()
closs = (v_pred - v_target) ** 2
if self.if_PER:
closs *= weight_batch
closs = closs.mean()
closs.backward()
torch.nn.utils.clip_grad_norm_(self.critic_policy_net.parameters(),1)
self.critic_optimizer.step()
self.actor_optimizer.zero_grad()
log_action_prob = self.actor_policy_net(pre_state_batch).log_prob(action_batch)
with torch.no_grad():
v_next_state = self.critic_policy_net(next_state_batch).detach()
v_target = self.gamma * v_next_state * (1 - if_end) + reward_batch
TD_error = v_target - self.critic_policy_net(pre_state_batch).detach()
aloss = -log_action_prob * TD_error
aloss = aloss.mean()
aloss.backward()
torch.nn.utils.clip_grad_norm_(self.actor_policy_net.parameters(),1)
self.actor_optimizer.step()
# update target network
self.soft_update(self.actor_target_net, self.actor_policy_net, self.tau)
self.soft_update(self.critic_target_net, self.critic_policy_net, self.tau)
self.global_step += 1
# store the (pre_s, action, reward, next_state, if_end) tuples in the replay memory
def perceive(self, pre_s, action, reward, next_state, if_end):
self.replay_mem.append([pre_s, action, reward, next_state, if_end])
if len(self.replay_mem) > self.mem_size:
self.replay_mem.popleft()
# use the policy net to choose the action with the highest Q value
def action(self, s, sample = True): # use flag to suit other models' action interface
s = torch.tensor(s, dtype=torch.float, device = self.device).unsqueeze(0)
with torch.no_grad():
m = self.actor_policy_net(s)
a = np.clip(m.sample(), self.action_low, self.action_high) if sample else m.mean
return a.numpy()[0]