问题描述
这是我对CartPole-v0的DQN和DDQN的实现，我认为是正确的。
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import gym
import torch.optim as optim
import random
import os
import time
class NETWORK(torch.nn.Module):
def __init__(self,input_dim: int,output_dim: int,hidden_dim: int) -> None:
super(NETWORK,self).__init__()
self.layer1 = torch.nn.Sequential(
torch.nn.Linear(input_dim,hidden_dim),torch.nn.ReLU()
)
self.layer2 = torch.nn.Sequential(
torch.nn.Linear(hidden_dim,torch.nn.ReLU()
)
self.final = torch.nn.Linear(hidden_dim,output_dim)
def forward(self,x: torch.Tensor) -> torch.Tensor:
x = self.layer1(x)
x = self.layer2(x)
x = self.final(x)
return x
class ReplayBuffer(object):
def __init__(self,capacity=50000):
self.capacity = capacity
self.memory = []
self.position = 0
def push(self,s0,a0,r,s1):
if len(self.memory) < self.capacity:
self.memory.append(None)
self.memory[self.position] = (s0,s1)
self.position = (self.position + 1) % self.capacity
def sample(self,batch_size=64):
return random.sample(self.memory,batch_size)
def __len__(self):
return len(self.memory)
class DQN(object):
def __init__(self):
self.state_dim = 4
self.action_dim = 2
self.lr = 0.001
self.discount_factor = 0.99
self.epsilon = 1
self.epsilon_decay = 0.95
self.num_train = 0
self.num_train_episodes = 0
self.batch_size = 64
self.predict_network = NETWORK(input_dim=4,output_dim=2,hidden_dim=16).double()
self.memory = ReplayBuffer(capacity=50000)
self.optimizer = torch.optim.Adam(self.predict_network.parameters(),lr=self.lr)
self.loss = 0
def select_action(self,states: np.ndarray) -> int:
if np.random.uniform(0,1) < self.epsilon:
return np.random.choice(self.action_dim)
else:
states = torch.from_numpy(states).unsqueeze_(dim=0)
with torch.no_grad():
Q_values = self.predict_network(states)
action = torch.argmax(Q_values).item()
return action
def policy(self,states: np.ndarray) -> int:
states = torch.from_numpy(states).unsqueeze_(dim=0)
with torch.no_grad():
Q_values = self.predict_network(states)
action = torch.argmax(Q_values).item()
return action
def train(self,s1,sign):
if sign == 1:
self.num_train_episodes += 1
if self.epsilon > 0.01:
self.epsilon = max(self.epsilon * self.epsilon_decay,0.01)
return
self.num_train += 1
self.memory.push(s0,s1)
if len(self.memory) < self.batch_size:
return
batch = self.memory.sample(self.batch_size)
state_batch = torch.from_numpy(np.stack([b[0] for b in batch]))
action_batch = torch.from_numpy(np.stack([b[1] for b in batch]))
reward_batch = torch.from_numpy(np.stack([b[2] for b in batch]))
next_state_batch = torch.from_numpy(np.stack([b[3] for b in batch]))
Q_values = self.predict_network(state_batch)[torch.arange(self.batch_size),action_batch]
next_state_Q_values = self.predict_network(next_state_batch).max(dim=1)[0]
Q_targets = self.discount_factor * next_state_Q_values + reward_batch
loss = F.mse_loss(Q_values,Q_targets.detach())
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.loss = loss.data.item()
class DDQN(object):
def __init__(self):
self.state_dim = 4
self.action_dim = 2
self.lr = 0.001
self.discount_factor = 0.9
self.epsilon = 1
self.epsilon_decay = 0.95
self.num_train = 0
self.num_train_episodes = 0
self.batch_size = 64
self.predict_network = NETWORK(input_dim=4,hidden_dim=16).double()
self.target_network = NETWORK(input_dim=4,hidden_dim=16).double()
self.target_network.load_state_dict(self.predict_network.state_dict())
self.target_network.eval()
self.memory = ReplayBuffer(capacity=50000)
self.optimizer = torch.optim.Adam(self.predict_network.parameters(),lr=self.lr)
self.loss = 0
def select_action(self,1) < self.epsilon:
return np.random.choice(self.action_dim)
else:
states = torch.from_numpy(states).unsqueeze_(dim=0)
with torch.no_grad():
Q_values = self.predict_network(states)
action = torch.argmax(Q_values).item()
return action
def policy(self,sign):
if sign == 1:
self.num_train_episodes += 1
if self.num_train_episodes % 2 == 0:
self.target_network.load_state_dict(self.predict_network.state_dict())
self.target_network.eval()
if self.epsilon > 0.01:
self.epsilon = max(self.epsilon * self.epsilon_decay,s1)
if len(self.memory) < self.batch_size:
return
batch = self.memory.sample(self.batch_size)
state_batch = torch.from_numpy(np.stack([b[0] for b in batch]))
action_batch = torch.from_numpy(np.stack([b[1] for b in batch]))
reward_batch = torch.from_numpy(np.stack([b[2] for b in batch]))
next_state_batch = torch.from_numpy(np.stack([b[3] for b in batch]))
Q_values = self.predict_network(state_batch)[torch.arange(self.batch_size),action_batch]
next_state_action_batch = torch.argmax(self.predict_network(next_state_batch),dim=1)
next_state_Q_values = self.target_network(next_state_batch)[torch.arange(self.batch_size),next_state_action_batch]
Q_targets = self.discount_factor * next_state_Q_values + reward_batch
loss = F.smooth_l1_loss(Q_values,Q_targets.detach())
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.loss = loss.data.item()
我使用以下方法评估和训练我的DQN和DDQN。
def eval_policy(agent,env_name,eval_episodes=10):
eval_env = gym.make(env_name)
avg_reward = 0.
for _ in range(eval_episodes):
state,done = eval_env.reset(),False
while not done:
action = agent.policy(state)
state,reward,done,_ = eval_env.step(action)
avg_reward += reward
avg_reward /= eval_episodes
print("---------------------------------------")
print(f"Evaluation over {eval_episodes} episodes: {avg_reward:.3f}")
print("---------------------------------------")
return avg_reward
env_name = 'CartPole-v0'
env = gym.make(env_name)
agent = DQN() # agent = DDQN()
for i in range(1000):
state,done = env.reset(),False
episodic_reward = 0
while not done:
action = agent.select_action(np.squeeze(state))
next_state,info = env.step(action)
episodic_reward += reward
sign = 1 if done else 0
agent.train(state,action,next_state,sign)
state = next_state
print(f'episode: {i},reward: {episodic_reward}')
if i % 20 == 0:
eval_reward = eval_policy(agent,eval_episodes=50)
if eval_reward >= 195:
print("Problem solved in {} episodes".format(i + 1))
break
问题是我的DQN网络没有进行训练，并且在损耗计算中使用target.detach（）导致损耗成指数增长。如果我不使用.detach（），DQN对象将进行训练，但是我认为这不是正确的方法。对于DDQN，我的网络始终不训练。任何人都可以针对哪里可能出问题提供一些建议吗？
解决方法
所以实现中的一个错误是，您永远不要将情节的结尾添加到重播缓冲区中。在火车功能中，如果sign == 1（情节结束），则返回。删除返回值，并通过（1-dones）* ...调整目标计算，以防您采样情节结尾的过渡。情节结尾之所以重要的原因是，这是唯一的经历，即无法通过引导来逼近目标。然后DQN训练。为了重现性，我使用0.99的贴现率和2020年的种子折扣（用于火炬，麻木和体育馆环境）。经过241次训练，我获得了199.100的奖励。
希望有帮助，顺便说一句，代码很可读。