import random
import gym
import numpy as np
import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
import rl_utils
from tqdm import tqdm
class Qnet(torch.nn.Module):
''' 只有一层隐藏层的Q网络 '''
def __init__(self, state_dim, hidden_dim, action_dim):
super(Qnet, self).__init__()
self.fc1 = torch.nn.Linear(state_dim, hidden_dim)
self.fc2 = torch.nn.Linear(hidden_dim, action_dim)
def forward(self, x):
x = F.relu(self.fc1(x))
return self.fc2(x)
class DQN:
''' DQN算法,包括Double DQN '''
def __init__(self,
state_dim,
hidden_dim,
action_dim,
learning_rate,
gamma,
epsilon,
target_update,
device,
dqn_type='VanillaDQN'):
self.action_dim = action_dim
self.q_net = Qnet(state_dim, hidden_dim, self.action_dim).to(device)
self.target_q_net = Qnet(state_dim, hidden_dim,
self.action_dim).to(device)
self.optimizer = torch.optim.Adam(self.q_net.parameters(),
lr=learning_rate)
self.gamma = gamma
self.epsilon = epsilon
self.target_update = target_update
self.count = 0
self.dqn_type = dqn_type
self.device = device
def take_action(self, state):
if np.random.random() < self.epsilon:
action = np.random.randint(self.action_dim)
else:
state = torch.tensor([state], dtype=torch.float).to(self.device)
action = self.q_net(state).argmax().item()
return action
def max_q_value(self, state):
state = torch.tensor([state], dtype=torch.float).to(self.device)
return self.q_net(state).max().item()
def update(self, transition_dict):
states = torch.tensor(transition_dict['states'],
dtype=torch.float).to(self.device)
actions = torch.tensor(transition_dict['actions']).view(-1, 1).to(
self.device)
rewards = torch.tensor(transition_dict['rewards'],
dtype=torch.float).view(-1, 1).to(self.device)
next_states = torch.tensor(transition_dict['next_states'],
dtype=torch.float).to(self.device)
dones = torch.tensor(transition_dict['dones'],
dtype=torch.float).view(-1, 1).to(self.device)
q_values = self.q_net(states).gather(1, actions) # Q值
# 下个状态的最大Q值
if self.dqn_type == 'DoubleDQN': # DQN与Double DQN的区别
max_action = self.q_net(next_states).max(1)[1].view(-1, 1)
a = self.target_q_net(next_states)
max_next_q_values = a.gather(1, max_action)
else: # DQN的情况
max_next_q_values = self.target_q_net(next_states).max(1)[0].view(-1, 1)
q_targets = rewards + self.gamma * max_next_q_values * (1 - dones) # TD误差目标
dqn_loss = torch.mean(F.mse_loss(q_values, q_targets)) # 均方误差损失函数
self.optimizer.zero_grad() # PyTorch中默认梯度会累积,这里需要显式将梯度置为0
dqn_loss.backward() # 反向传播更新参数
self.optimizer.step()
if self.count % self.target_update == 0:
self.target_q_net.load_state_dict(
self.q_net.state_dict()) # 更新目标网络
self.count += 1
def train_DQN(agent, env, num_episodes, replay_buffer, minimal_size,
batch_size):
return_list = []
max_q_value_list = []
max_q_value = 0
for i in range(10):
with tqdm(total=int(num_episodes / 10),
desc='Iteration %d' % i) as pbar:
for i_episode in range(int(num_episodes / 10)):
episode_return = 0
state = env.reset()[0]
done = False
while not done:
action = agent.take_action(state)
max_q_value = agent.max_q_value(
state) * 0.005 + max_q_value * 0.995 # 平滑处理
max_q_value_list.append(max_q_value) # 保存每个状态的最大Q值
action_continuous = dis_to_con(action, env,
agent.action_dim)
next_state, reward, done, _,_ = env.step([action_continuous])
replay_buffer.add(state, action, reward, next_state, done)
state = next_state
episode_return += reward
if replay_buffer.size() > minimal_size:
b_s, b_a, b_r, b_ns, b_d = replay_buffer.sample(
batch_size)
transition_dict = {
'states': b_s,
'actions': b_a,
'next_states': b_ns,
'rewards': b_r,
'dones': b_d
}
agent.update(transition_dict)
return_list.append(episode_return)
if (i_episode + 1) % 10 == 0:
pbar.set_postfix({
'episode':
'%d' % (num_episodes / 10 * i + i_episode + 1),
'return':
'%.3f' % np.mean(return_list[-10:])
})
pbar.update(1)
return return_list, max_q_value_list
def run_DoubleDQN():
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
replay_buffer = rl_utils.ReplayBuffer(buffer_size)
agent = DQN(state_dim, hidden_dim, action_dim, lr, gamma, epsilon,
target_update, device, 'DoubleDQN')
return_list, max_q_value_list = train_DQN(agent, env, num_episodes,
replay_buffer, minimal_size,
batch_size)
episodes_list = list(range(len(return_list)))
mv_return = rl_utils.moving_average(return_list, 5)
plt.plot(episodes_list, mv_return)
plt.xlabel('Episodes')
plt.ylabel('Returns')
plt.title('Double DQN on {}'.format(env_name))
plt.show()
frames_list = list(range(len(max_q_value_list)))
plt.plot(frames_list, max_q_value_list)
plt.axhline(0, c='orange', ls='--')
plt.axhline(10, c='red', ls='--')
plt.xlabel('Frames')
plt.ylabel('Q value')
plt.title('Double DQN on {}'.format(env_name))
plt.show()
def run_dqn():
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
replay_buffer = rl_utils.ReplayBuffer(buffer_size)
agent = DQN(state_dim, hidden_dim, action_dim, lr, gamma, epsilon,
target_update, device)
return_list, max_q_value_list = train_DQN(agent, env, num_episodes,
replay_buffer, minimal_size,
batch_size)
episodes_list = list(range(len(return_list)))
mv_return = rl_utils.moving_average(return_list, 5)
plt.plot(episodes_list, mv_return)
plt.xlabel('Episodes')
plt.ylabel('Returns')
plt.title('DQN on {}'.format(env_name))
plt.show()
frames_list = list(range(len(max_q_value_list)))
plt.plot(frames_list, max_q_value_list)
plt.axhline(0, c='orange', ls='--')
plt.axhline(10, c='red', ls='--')
plt.xlabel('Frames')
plt.ylabel('Q value')
plt.title('DQN on {}'.format(env_name))
plt.show()
if __name__ == '__main__':
lr = 1e-2
num_episodes = 200
hidden_dim = 128
gamma = 0.98
epsilon = 0.01
target_update = 50
buffer_size = 5000
minimal_size = 1000
batch_size = 64
device = torch.device("cuda") if torch.cuda.is_available() else torch.device(
"cpu")
env_name = 'Pendulum-v1'
env = gym.make(env_name)
state_dim = env.observation_space.shape[0]
action_dim = 11 # 将连续动作分成11个离散动作
def dis_to_con(discrete_action, env, action_dim): # 离散动作转回连续的函数
action_lowbound = env.action_space.low[0] # 连续动作的最小值
action_upbound = env.action_space.high[0] # 连续动作的最大值
return action_lowbound + (discrete_action /
(action_dim - 1)) * (action_upbound -
action_lowbound)
run_DoubleDQN()
