1、使用Pytorch基于DQN的实现

1.1 主要参考

(1)推荐pytorch官方的教程

Reinforcement Learning (DQN) Tutorial — PyTorch Tutorials 2.0.1+cu117 documentation

(2)

Pytorch 深度强化学习 – CartPole问题|极客笔记

2.2 pytorch官方的教程原理

待续，这两天时期多，过两天整理一下。

2.3代码实现

import gymnasium as gym
import math
import random
import matplotlib
import matplotlib.pyplot as plt
from collections import namedtuple, deque
from itertools import countimport torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as Fenv = gym.make("CartPole-v1")# set up matplotlib
# is_ipython = 'inline' in matplotlib.get_backend()
# if is_ipython:
#     from IPython import displayplt.ion()# if GPU is to be used
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")Transition = namedtuple('Transition',('state', 'action', 'next_state', 'reward'))class ReplayMemory(object):def __init__(self, capacity):self.memory = deque([], maxlen=capacity)def push(self, *args):"""Save a transition"""self.memory.append(Transition(*args))def sample(self, batch_size):return random.sample(self.memory, batch_size)def __len__(self):return len(self.memory)class DQN(nn.Module):def __init__(self, n_observations, n_actions):super(DQN, self).__init__()self.layer1 = nn.Linear(n_observations, 128)self.layer2 = nn.Linear(128, 128)self.layer3 = nn.Linear(128, n_actions)# Called with either one element to determine next action, or a batch# during optimization. Returns tensor([[left0exp,right0exp]...]).def forward(self, x):x = F.relu(self.layer1(x))x = F.relu(self.layer2(x))return self.layer3(x)# BATCH_SIZE is the number of transitions sampled from the replay buffer
# GAMMA is the discount factor as mentioned in the previous section
# EPS_START is the starting value of epsilon
# EPS_END is the final value of epsilon
# EPS_DECAY controls the rate of exponential decay of epsilon, higher means a slower decay
# TAU is the update rate of the target network
# LR is the learning rate of the ``AdamW`` optimizer
BATCH_SIZE = 128
GAMMA = 0.99
EPS_START = 0.9
EPS_END = 0.05
EPS_DECAY = 1000
TAU = 0.005
LR = 1e-4# Get number of actions from gym action space
n_actions = env.action_space.n
# Get the number of state observations
state, info = env.reset()
n_observations = len(state)policy_net = DQN(n_observations, n_actions).to(device)
target_net = DQN(n_observations, n_actions).to(device)
target_net.load_state_dict(policy_net.state_dict())optimizer = optim.AdamW(policy_net.parameters(), lr=LR, amsgrad=True)
memory = ReplayMemory(10000)steps_done = 0def select_action(state):global steps_donesample = random.random()eps_threshold = EPS_END + (EPS_START - EPS_END) * \math.exp(-1. * steps_done / EPS_DECAY)steps_done += 1if sample > eps_threshold:with torch.no_grad():# t.max(1) will return the largest column value of each row.# second column on max result is index of where max element was# found, so we pick action with the larger expected reward.return policy_net(state).max(1)[1].view(1, 1)else:return torch.tensor([[env.action_space.sample()]], device=device, dtype=torch.long)episode_durations = []def plot_durations(show_result=False):plt.figure(1)durations_t = torch.tensor(episode_durations, dtype=torch.float)if show_result:plt.title('Result')else:plt.clf()plt.title('Training...')plt.xlabel('Episode')plt.ylabel('Duration')plt.plot(durations_t.numpy())# Take 100 episode averages and plot them tooif len(durations_t) >= 100:means = durations_t.unfold(0, 100, 1).mean(1).view(-1)means = torch.cat((torch.zeros(99), means))plt.plot(means.numpy())plt.pause(0.001)  # pause a bit so that plots are updated# if is_ipython:#     if not show_result:#         display.display(plt.gcf())#         display.clear_output(wait=True)#     else:#         display.display(plt.gcf())def optimize_model():if len(memory) < BATCH_SIZE:returntransitions = memory.sample(BATCH_SIZE)# Transpose the batch (see https://stackoverflow.com/a/19343/3343043 for# detailed explanation). This converts batch-array of Transitions# to Transition of batch-arrays.batch = Transition(*zip(*transitions))# Compute a mask of non-final states and concatenate the batch elements# (a final state would've been the one after which simulation ended)non_final_mask = torch.tensor(tuple(map(lambda s: s is not None,batch.next_state)), device=device, dtype=torch.bool)non_final_next_states = torch.cat([s for s in batch.next_stateif s is not None])state_batch = torch.cat(batch.state)action_batch = torch.cat(batch.action)reward_batch = torch.cat(batch.reward)# Compute Q(s_t, a) - the model computes Q(s_t), then we select the# columns of actions taken. These are the actions which would've been taken# for each batch state according to policy_netstate_action_values = policy_net(state_batch).gather(1, action_batch)# Compute V(s_{t+1}) for all next states.# Expected values of actions for non_final_next_states are computed based# on the "older" target_net; selecting their best reward with max(1)[0].# This is merged based on the mask, such that we'll have either the expected# state value or 0 in case the state was final.next_state_values = torch.zeros(BATCH_SIZE, device=device)with torch.no_grad():next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0]# Compute the expected Q valuesexpected_state_action_values = (next_state_values * GAMMA) + reward_batch# Compute Huber losscriterion = nn.SmoothL1Loss()loss = criterion(state_action_values, expected_state_action_values.unsqueeze(1))# Optimize the modeloptimizer.zero_grad()loss.backward()# In-place gradient clippingtorch.nn.utils.clip_grad_value_(policy_net.parameters(), 100)optimizer.step()if torch.cuda.is_available():num_episodes = 600
else:# num_episodes = 50num_episodes = 600for i_episode in range(num_episodes):# Initialize the environment and get it's statestate, info = env.reset()state = torch.tensor(state, dtype=torch.float32, device=device).unsqueeze(0)for t in count():action = select_action(state)observation, reward, terminated, truncated, _ = env.step(action.item())reward = torch.tensor([reward], device=device)done = terminated or truncatedif terminated:next_state = Noneelse:next_state = torch.tensor(observation, dtype=torch.float32, device=device).unsqueeze(0)# Store the transition in memorymemory.push(state, action, next_state, reward)# Move to the next statestate = next_state# Perform one step of the optimization (on the policy network)optimize_model()# Soft update of the target network's weights# θ′ ← τ θ + (1 −τ )θ′target_net_state_dict = target_net.state_dict()policy_net_state_dict = policy_net.state_dict()for key in policy_net_state_dict:target_net_state_dict[key] = policy_net_state_dict[key]*TAU + target_net_state_dict[key]*(1-TAU)target_net.load_state_dict(target_net_state_dict)if done:episode_durations.append(t + 1)plot_durations()breakprint('Complete')
plot_durations(show_result=True)
plt.ioff()
plt.show()