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DDPG.py
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DDPG.py
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import torch
import numpy as np
from Torch_rl.common.memory import ReplayMemory
from Torch_rl.agent.core_value import Agent_value_based
from copy import deepcopy
from torch.optim import Adam
from torch import nn
from Torch_rl.common.loss import huber_loss
from torch.autograd import Variable
class actor_critic(nn.Module):
def __init__(self, actor, critic):
super(actor_critic, self).__init__()
self.actor = actor
self.critic = critic
def forward(self, obs):
a = self.actor(obs)
input = torch.cat((obs, a), axis=-1)
Q = self.critic(input)
return Q
class DDPG_Agent(Agent_value_based):
def __init__(self, env, actor_model, critic_model,
actor_lr=1e-4, critic_lr=1e-3,
actor_target_network_update_freq=1000, critic_target_network_update_freq=1000,
actor_training_freq=1, critic_training_freq=1,
## hyper-parameter
gamma=0.99, batch_size=32, buffer_size=50000, learning_starts=1000,
## lr_decay
decay=False, decay_rate=0.9, critic_l2_reg=1e-2, clip_norm =None,
##
path=None):
self.gpu = False
self.env = env
self.gamma = gamma
self.batch_size = batch_size
self.learning_starts = learning_starts
self.replay_buffer = ReplayMemory(buffer_size)
self.actor_training_freq, self.critic_training_freq = actor_training_freq, critic_training_freq
self.actor_target_network_update_freq = actor_target_network_update_freq
self.critic_target_network_update_freq = critic_target_network_update_freq
self.actor = actor_model
self.critic = critic_model
self.target_actor = deepcopy(actor_model)
self.target_critic = deepcopy(critic_model)
self.actor_critic = actor_critic(self.actor, self.critic)
actor_optim = Adam(self.actor.parameters(), lr=actor_lr)
critic_optim = Adam(self.critic.parameters(), lr=critic_lr, weight_decay=critic_l2_reg)
if decay:
self.actor_optim = torch.optim.lr_scheduler.ExponentialLR(actor_optim, decay_rate, last_epoch=-1)
self.critic_optim = torch.optim.lr_scheduler.ExponentialLR(critic_optim, decay_rate, last_epoch=-1)
else:
self.actor_optim = actor_optim
self.critic_optim = critic_optim
super(DDPG_Agent, self).__init__(path)
example_input = Variable(torch.rand(100, self.env.observation_space.shape[0]))
self.writer.add_graph(self.actor_critic, input_to_model=example_input)
self.forward_step_show_list = []
self.backward_step_show_list =[]
self.forward_ep_show_list = []
self.backward_ep_show_list = []
def forward(self, observation):
observation = observation[np.newaxis, :].astype(np.float32)
observation = torch.from_numpy(observation)
action = self.actor.forward(observation)
action = torch.normal(action, torch.ones_like(action))
Q = self.critic(torch.cat((observation, action), dim=1)).squeeze().detach().numpy()
return action.cpu().squeeze(0).detach().numpy(), Q, {}
def backward(self, sample_):
self.replay_buffer.push(sample_)
if self.step > self.learning_starts and self.learning:
sample = self.replay_buffer.sample(self.batch_size)
if self.gpu:
for key in sample.keys():
sample[key] = sample[key].cuda()
assert len(sample["s"]) == self.batch_size
"update the critic "
if self.step % self.critic_training_freq == 0:
input = torch.cat((sample["s"], sample["a"]), -1)
Q = self.critic(input)
target_a = self.target_actor(sample["s_"])
target_input = torch.cat((sample["s_"], target_a), -1)
targetQ = self.target_critic(target_input)
targetQ = targetQ.squeeze(1)
Q = Q.squeeze(1)
expected_q_values = sample["r"] + self.gamma * targetQ * (1.0 - sample["tr"])
loss = torch.mean(huber_loss(expected_q_values - Q))
self.critic_optim.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.critic.parameters(), 1, norm_type=2)
self.critic_optim.step()
"training the actor"
if self.step % self.actor_training_freq == 0:
# Q = self.critic(torch.cat((sample["s"], self.actor(sample["s"])), -1))
Q = self.actor_critic.forward(sample["s"])
Q = -torch.mean(Q)
self.actor_optim.zero_grad()
Q.backward()
torch.nn.utils.clip_grad_norm_(self.actor_critic.parameters(), 1, norm_type=2)
self.actor_optim.step()
if self.step % self.actor_target_network_update_freq == 0:
self.target_actor_net_update()
if self.step % self.critic_target_network_update_freq == 0:
self.target_critic_net_update()
loss = loss.data.numpy()
return loss, {}
return 0, {}
def target_actor_net_update(self):
self.target_actor.load_state_dict(self.actor.state_dict())
def target_critic_net_update(self):
self.target_critic.load_state_dict(self.critic.state_dict())
def load_weights(self, filepath):
model = torch.load(filepath)
self.actor.load_state_dict(model["actor"])
self.critic.load_state_dict(model["critic"])
self.target_actor.load_state_dict(model["target_actor"])
self.target_critic.load_state_dict(model["target_critic"])
self.actor_optim.load_state_dict(model["actor_optim"])
self.critic_optim.load_state_dict(model["critic_optim"])
def save_weights(self, filepath, overwrite=False):
torch.save({"actor": self.actor, "critic":self.critic,
"target_actor": self.target_actor,"target_critic": self.target_critic,
"actor_optim": self.actor_optim, "critic_optim": self.critic_optim
}, filepath + "DDPG.pkl")
def cuda(self):
self.actor.to_gpu()
self.critic.to_gpu()
self.target_actor = deepcopy(self.actor)
self.target_critic = deepcopy(self.critic)
self.gpu = True