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强化学习从根底到进阶 – 案例与实际[7.1]：深度确定性策略梯度 DDPG 算法、双提早深度确定性策略梯度 TD3 算法详解我的项目实战
1、定义算法
1.1 定义模型
!pip uninstall -y parl
!pip install parl
import parl
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
class Actor(parl.Model):
def __init__(self, n_states, n_actions):
super(Actor, self).__init__()
self.l1 = nn.Linear(n_states, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, n_actions)
def forward(self, state):
x = F.relu(self.l1(state))
x = F.relu(self.l2(x))
return paddle.tanh(self.l3(x))
class Critic(parl.Model):
def __init__(self, n_states, n_actions):
super(Critic, self).__init__()
self.l1 = nn.Linear(n_states, 400)
self.l2 = nn.Linear(400 + n_actions, 300)
self.l3 = nn.Linear(300, 1)
def forward(self, state, action):
x = F.relu(self.l1(state))
x = F.relu(self.l2(paddle.concat([x, action], 1)))
return self.l3(x)
class ActorCritic(parl.Model):
def __init__(self, n_states, n_actions):
super(ActorCritic, self).__init__()
self.actor_model = Actor(n_states, n_actions)
self.critic_model = Critic(n_states, n_actions)
def policy(self, state):
return self.actor_model(state)
def value(self, state, action):
return self.critic_model(state, action)
def get_actor_params(self):
return self.actor_model.parameters()
def get_critic_params(self):
return self.critic_model.parameters()1.2 定义教训回放
from collections import deque
import random
class ReplayBuffer:
def __init__(self, capacity: int) -> None:
self.capacity = capacity
self.buffer = deque(maxlen=self.capacity)
def push(self,transitions):
'''_summary_
Args:
trainsitions (tuple): _description_
'''
self.buffer.append(transitions)
def sample(self, batch_size: int, sequential: bool = False):
if batch_size > len(self.buffer):
batch_size = len(self.buffer)
if sequential: # sequential sampling
rand = random.randint(0, len(self.buffer) - batch_size)
batch = [self.buffer[i] for i in range(rand, rand + batch_size)]
return zip(*batch)
else:
batch = random.sample(self.buffer, batch_size)
return zip(*batch)
def clear(self):
self.buffer.clear()
def __len__(self):
return len(self.buffer)1.3 定义智能体
import parl
import paddle
import numpy as np
class DDPGAgent(parl.Agent):
def __init__(self, algorithm,memory,cfg):
super(DDPGAgent, self).__init__(algorithm)
self.n_actions = cfg['n_actions']
self.expl_noise = cfg['expl_noise']
self.batch_size = cfg['batch_size']
self.memory = memory
self.alg.sync_target(decay=0)
def sample_action(self, state):
action_numpy = self.predict_action(state)
action_noise = np.random.normal(0, self.expl_noise, size=self.n_actions)
action = (action_numpy + action_noise).clip(-1, 1)
return action
def predict_action(self, state):
state = paddle.to_tensor(state.reshape(1, -1), dtype='float32')
action = self.alg.predict(state)
action_numpy = action.cpu().numpy()[0]
return action_numpy
def update(self):
if len(self.memory) < self.batch_size:
return
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(self.batch_size)
done_batch = np.expand_dims(done_batch , -1)
reward_batch = np.expand_dims(reward_batch, -1)
state_batch = paddle.to_tensor(state_batch, dtype='float32')
action_batch = paddle.to_tensor(action_batch, dtype='float32')
reward_batch = paddle.to_tensor(reward_batch, dtype='float32')
next_state_batch = paddle.to_tensor(next_state_batch, dtype='float32')
done_batch = paddle.to_tensor(done_batch, dtype='float32')
critic_loss, actor_loss = self.alg.learn(state_batch, action_batch, reward_batch, next_state_batch,
done_batch)2. 定义训练
def train(cfg, env, agent):
'''训练'''
print(f"开始训练！")
rewards = [] # 记录所有回合的处分
for i_ep in range(cfg["train_eps"]):
ep_reward = 0
state = env.reset()
for i_step in range(cfg['max_steps']):
action = agent.sample_action(state) # 采样动作
next_state, reward, done, _ = env.step(action)
agent.memory.push((state, action, reward,next_state, done))
state = next_state
agent.update()
ep_reward += reward
if done:
break
rewards.append(ep_reward)
if (i_ep + 1) % 10 == 0:
print(f"回合：{i_ep+1}/{cfg['train_eps']}，处分：{ep_reward:.2f}")
print("实现训练！")
env.close()
res_dic = {'episodes':range(len(rewards)),'rewards':rewards}
return res_dic
def test(cfg, env, agent):
print("开始测试！")
rewards = [] # 记录所有回合的处分
for i_ep in range(cfg['test_eps']):
ep_reward = 0
state = env.reset()
for i_step in range(cfg['max_steps']):
action = agent.predict_action(state)
next_state, reward, done, _ = env.step(action)
state = next_state
ep_reward += reward
if done:
break
rewards.append(ep_reward)
print(f"回合：{i_ep+1}/{cfg['test_eps']}，处分：{ep_reward:.2f}")
print("实现测试！")
env.close()
return {'episodes':range(len(rewards)),'rewards':rewards}3、定义环境
OpenAI Gym 中其实集成了很多强化学习环境，足够大家学习了，然而在做强化学习的利用中免不了要本人创立环境，比方在本我的项目中其实不太好找到 Qlearning 能学进去的环境，Qlearning 切实是太弱了，须要足够简略的环境才行，因而本我的项目写了一个环境，大家感兴趣的话能够看一下，个别环境接口最要害的局部即便 reset 和 step。
import gym
import os
import paddle
import numpy as np
import random
from parl.algorithms import DDPG
class NormalizedActions(gym.ActionWrapper):
'''将 action 范畴重定在 [0.1] 之间'''
def action(self, action):
low_bound = self.action_space.low
upper_bound = self.action_space.high
action = low_bound + (action + 1.0) * 0.5 * (upper_bound - low_bound)
action = np.clip(action, low_bound, upper_bound)
return action
def reverse_action(self, action):
low_bound = self.action_space.low
upper_bound = self.action_space.high
action = 2 * (action - low_bound) / (upper_bound - low_bound) - 1
action = np.clip(action, low_bound, upper_bound)
return action
def all_seed(env,seed = 1):
'''万能的 seed 函数'''
env.seed(seed) # env config
np.random.seed(seed)
random.seed(seed)
paddle.seed(seed)
def env_agent_config(cfg):
env = NormalizedActions(gym.make(cfg['env_name'])) # 装璜 action 噪声
if cfg['seed'] !=0:
all_seed(env,seed=cfg['seed'])
n_states = env.observation_space.shape[0]
n_actions = env.action_space.shape[0]
print(f"状态维度：{n_states}，动作维度：{n_actions}")
cfg.update({"n_states":n_states,"n_actions":n_actions}) # 更新 n_states 和 n_actions 到 cfg 参数中
memory = ReplayBuffer(cfg['memory_capacity'])
model = ActorCritic(n_states, n_actions)
algorithm = DDPG(model, gamma=cfg['gamma'], tau=cfg['tau'], actor_lr=cfg['actor_lr'], critic_lr=cfg['critic_lr'])
agent = DDPGAgent(algorithm,memory,cfg)
return env,agent4、设置参数
到这里所有 qlearning 模块就算实现了，上面须要设置一些参数，不便大家“炼丹”，其中默认的是笔者曾经调好的～。另外为了定义了一个画图函数，用来形容处分的变动。
import argparse
import matplotlib.pyplot as plt
import seaborn as sns
def get_args():
"""超参数"""
parser = argparse.ArgumentParser(description="hyperparameters")
parser.add_argument('--algo_name',default='DDPG',type=str,help="name of algorithm")
parser.add_argument('--env_name',default='Pendulum-v0',type=str,help="name of environment")
parser.add_argument('--train_eps',default=200,type=int,help="episodes of training")
parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
parser.add_argument('--max_steps',default=100000,type=int,help="steps per episode, much larger value can simulate infinite steps")
parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
parser.add_argument('--critic_lr',default=1e-3,type=float,help="learning rate of critic")
parser.add_argument('--actor_lr',default=1e-4,type=float,help="learning rate of actor")
parser.add_argument('--memory_capacity',default=80000,type=int,help="memory capacity")
parser.add_argument('--expl_noise',default=0.1,type=float)
parser.add_argument('--batch_size',default=128,type=int)
parser.add_argument('--target_update',default=2,type=int)
parser.add_argument('--tau',default=1e-2,type=float)
parser.add_argument('--critic_hidden_dim',default=256,type=int)
parser.add_argument('--actor_hidden_dim',default=256,type=int)
parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda")
parser.add_argument('--seed',default=1,type=int,help="random seed")
args = parser.parse_args([])
args = {**vars(args)} # 将 args 转换为字典
# 打印参数
print("训练参数如下：")
print(''.join(['=']*80))
tplt = "{:^20}\t{:^20}\t{:^20}"
print(tplt.format("参数名","参数值","参数类型"))
for k,v in args.items():
print(tplt.format(k,v,str(type(v))))
print(''.join(['=']*80))
return args
def smooth(data, weight=0.9):
''' 用于平滑曲线，相似于 Tensorboard 中的 smooth
Args:
data (List): 输出数据
weight (Float): 平滑权重，处于 0 - 1 之间，数值越高阐明越平滑，个别取 0.9
Returns:
smoothed (List): 平滑后的数据
'''
last = data[0] # First value in the plot (first timestep)
smoothed = list()
for point in data:
smoothed_val = last * weight + (1 - weight) * point # 计算平滑值
smoothed.append(smoothed_val)
last = smoothed_val
return smoothed
def plot_rewards(rewards,cfg,path=None,tag='train'):
sns.set()
plt.figure() # 创立一个图形实例，不便同时多画几个图
plt.title(f"{tag}ing curve on {cfg['device']} of {cfg['algo_name']} for {cfg['env_name']}")
plt.xlabel('epsiodes')
plt.plot(rewards, label='rewards')
plt.plot(smooth(rewards), label='smoothed')
plt.legend()
5、训练
# 获取参数
cfg = get_args()
# 训练
env, agent = env_agent_config(cfg)
res_dic = train(cfg, env, agent)
plot_rewards(res_dic['rewards'], cfg, tag="train")
# 测试
res_dic = test(cfg, env, agent)
plot_rewards(res_dic['rewards'], cfg, tag="test") # 画出后果训练参数如下：================================================================================
参数名 参数值 参数类型
algo_name DDPG <class 'str'>
env_name Pendulum-v0 <class 'str'>
train_eps 200 <class 'int'>
test_eps 20 <class 'int'>
max_steps 100000 <class 'int'>
gamma 0.99 <class 'float'>
critic_lr 0.001 <class 'float'>
actor_lr 0.0001 <class 'float'>
memory_capacity 80000 <class 'int'>
expl_noise 0.1 <class 'float'>
batch_size 128 <class 'int'>
target_update 2 <class 'int'>
tau 0.01 <class 'float'>
critic_hidden_dim 256 <class 'int'>
actor_hidden_dim 256 <class 'int'>
device cpu <class 'str'>
seed 1 <class 'int'>
================================================================================
状态维度：3，动作维度：1
开始训练！回合：10/200，处分：-922.80
回合：20/200，处分：-390.80
回合：30/200，处分：-125.50
回合：40/200，处分：-822.66
回合：50/200，处分：-384.92
回合：60/200，处分：-132.26
回合：70/200，处分：-240.20
回合：80/200，处分：-242.37
回合：90/200，处分：-127.13
回合：100/200，处分：-365.29
回合：110/200，处分：-126.27
回合：120/200，处分：-231.47
回合：130/200，处分：-1.98
回合：140/200，处分：-223.84
回合：150/200，处分：-123.29
回合：160/200，处分：-362.06
回合：170/200，处分：-126.93
回合：180/200，处分：-119.77
回合：190/200，处分：-114.72
回合：200/200，处分：-116.01
实现训练！开始测试！回合：1/20，处分：-125.61
回合：2/20，处分：-0.97
回合：3/20，处分：-130.02
回合：4/20，处分：-117.46
回合：5/20，处分：-128.45
回合：6/20，处分：-124.48
回合：7/20，处分：-118.31
回合：8/20，处分：-127.18
回合：9/20，处分：-118.09
回合：10/20，处分：-0.55
回合：11/20，处分：-117.72
回合：12/20，处分：-1.08
回合：13/20，处分：-124.74
回合：14/20，处分：-133.55
回合：15/20，处分：-234.81
回合：16/20，处分：-126.93
回合：17/20，处分：-128.20
回合：18/20，处分：-124.76
回合：19/20，处分：-119.91
回合：20/20，处分：-287.89
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