问题描述
我正在使用 Keras 构建 ddpg 模型，我按照这里的官方说明输入 link description here
但我将健身房环境更改为我自己的环境：
import tensorflow as tf
from tensorflow.keras import layers
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing,CSV file I/O (e.g. pd.read_csv)
from subprocess import check_output
# print(check_output(["ls","../input"]).decode("utf8"))
import time
import copy
import numpy as np
import pandas as pd
data = pd.read_csv (r'C:\Users\willi\Downloads\spyv.csv')
data = data.loc[:,~data.columns.str.contains('^Unnamed')]
date_split = 377
train = data[:date_split]
test = data[date_split:]
class Environment1:
def __init__(self,data,history_t=90):
self.data = data
self.history_t = history_t
self.reset ()
def reset(self):
self.t = 0
self.done = False
self.profits = 0
self.positions = []
self.position_value = 0
self.history = [0 for _ in range (self.history_t)]
return [self.position_value] + self.history # obs
def step(self,act):
reward = 0
# act = 0: stay,1: buy,2: sell
if act == 1:
self.positions.append (self.data.iloc[self.t,:]['close'])
elif act == 2: # sell
if len (self.positions) == 0:
reward = -1
else:
profits = 0
for p in self.positions:
profits += (self.data.iloc[self.t,:]['close'] - p)
reward += profits
self.profits += profits
self.positions = []
# set next time
self.t += 1
self.position_value = 0
for p in self.positions:
self.position_value += (self.data.iloc[self.t,:]['close'] - p)
self.history.pop (0)
self.history.append (self.data.iloc[self.t,:]['close'] - self.data.iloc[(self.t - 1),:]['close'])
# clipping reward
if reward > 0:
reward = 1
elif reward < 0:
reward = -1
return [self.position_value] + self.history,reward,self.done # obs,done
env = Environment1 (train)
print (env.reset ())
for _ in range (3):
pact = np.random.randint (3)
print (env.step (pact))
#above here are all my own code,under here is the code from keras
num_states = env.observation_space.shape[0]
print("Size of State Space -> {}".format(num_states))
num_actions = env.action_space.shape[0]
print("Size of Action Space -> {}".format(num_actions))
upper_bound = env.action_space.high[0]
lower_bound = env.action_space.low[0]
print("Max Value of Action -> {}".format(upper_bound))
print("Min Value of Action -> {}".format(lower_bound))
class OUActionNoise:
def __init__(self,mean,std_deviation,theta=0.15,dt=1e-2,x_initial=None):
self.theta = theta
self.mean = mean
self.std_dev = std_deviation
self.dt = dt
self.x_initial = x_initial
self.reset()
def __call__(self):
# Formula taken from https://www.wikipedia.org/wikI/Ornstein-Uhlenbeck_process.
x = (
self.x_prev
+ self.theta * (self.mean - self.x_prev) * self.dt
+ self.std_dev * np.sqrt(self.dt) * np.random.normal(size=self.mean.shape)
)
# Store x into x_prev
# Makes next noise dependent on current one
self.x_prev = x
return x
def reset(self):
if self.x_initial is not None:
self.x_prev = self.x_initial
else:
self.x_prev = np.zeros_like(self.mean)
class Buffer:
def __init__(self,buffer_capacity=100000,batch_size=64):
# Number of "experiences" to store at max
self.buffer_capacity = buffer_capacity
# Num of tuples to train on.
self.batch_size = batch_size
# Its tells us num of times record() was called.
self.buffer_counter = 0
# Instead of list of tuples as the exp.replay concept go
# We use different np.arrays for each tuple element
self.state_buffer = np.zeros((self.buffer_capacity,num_states))
self.action_buffer = np.zeros((self.buffer_capacity,num_actions))
self.reward_buffer = np.zeros((self.buffer_capacity,1))
self.next_state_buffer = np.zeros((self.buffer_capacity,num_states))
# Takes (s,a,r,s') obervation tuple as input
def record(self,obs_tuple):
# Set index to zero if buffer_capacity is exceeded,# replacing old records
index = self.buffer_counter % self.buffer_capacity
self.state_buffer[index] = obs_tuple[0]
self.action_buffer[index] = obs_tuple[1]
self.reward_buffer[index] = obs_tuple[2]
self.next_state_buffer[index] = obs_tuple[3]
self.buffer_counter += 1
# Eager execution is turned on by default in TensorFlow 2. Decorating with tf.function allows
# TensorFlow to build a static graph out of the logic and computations in our function.
# This provides a large speed up for blocks of code that contain many small TensorFlow operations such as this one.
@tf.function
def update(
self,state_batch,action_batch,reward_batch,next_state_batch,):
# Training and updating Actor & Critic networks.
# See Pseudo Code.
with tf.GradientTape() as tape:
target_actions = target_actor(next_state_batch,training=True)
y = reward_batch + gamma * target_critic(
[next_state_batch,target_actions],training=True
)
critic_value = critic_model([state_batch,action_batch],training=True)
critic_loss = tf.math.reduce_mean(tf.math.square(y - critic_value))
critic_grad = tape.gradient(critic_loss,critic_model.trainable_variables)
critic_optimizer.apply_gradients(
zip(critic_grad,critic_model.trainable_variables)
)
with tf.GradientTape() as tape:
actions = actor_model(state_batch,training=True)
critic_value = critic_model([state_batch,actions],training=True)
# Used `-value` as we want to maximize the value given
# by the critic for our actions
actor_loss = -tf.math.reduce_mean(critic_value)
actor_grad = tape.gradient(actor_loss,actor_model.trainable_variables)
actor_optimizer.apply_gradients(
zip(actor_grad,actor_model.trainable_variables)
)
# We compute the loss and update parameters
def learn(self):
# Get sampling range
record_range = min(self.buffer_counter,self.buffer_capacity)
# Randomly sample indices
batch_indices = np.random.choice(record_range,self.batch_size)
# Convert to tensors
state_batch = tf.convert_to_tensor(self.state_buffer[batch_indices])
action_batch = tf.convert_to_tensor(self.action_buffer[batch_indices])
reward_batch = tf.convert_to_tensor(self.reward_buffer[batch_indices])
reward_batch = tf.cast(reward_batch,dtype=tf.float32)
next_state_batch = tf.convert_to_tensor(self.next_state_buffer[batch_indices])
self.update(state_batch,next_state_batch)
# This update target parameters slowly
# Based on rate `tau`,which is much less than one.
@tf.function
def update_target(target_weights,weights,tau):
for (a,b) in zip(target_weights,weights):
a.assign(b * tau + a * (1 - tau))
def get_actor():
# Initialize weights between -3e-3 and 3-e3
last_init = tf.random_uniform_initializer(minval=-0.003,maxval=0.003)
inputs = layers.Input(shape=(num_states,))
out = layers.Dense(256,activation="relu")(inputs)
out = layers.Dense(256,activation="relu")(out)
outputs = layers.Dense(1,activation="tanh",kernel_initializer=last_init)(out)
# Our upper bound is 2.0 for Pendulum.
outputs = outputs * upper_bound
model = tf.keras.Model(inputs,outputs)
return model
def get_critic():
# State as input
state_input = layers.Input(shape=(num_states))
state_out = layers.Dense(16,activation="relu")(state_input)
state_out = layers.Dense(32,activation="relu")(state_out)
# Action as input
action_input = layers.Input(shape=(num_actions))
action_out = layers.Dense(32,activation="relu")(action_input)
# Both are passed through seperate layer before concatenating
concat = layers.Concatenate()([state_out,action_out])
out = layers.Dense(256,activation="relu")(concat)
out = layers.Dense(256,activation="relu")(out)
outputs = layers.Dense(1)(out)
# Outputs single value for give state-action
model = tf.keras.Model([state_input,action_input],outputs)
return model
def policy(state,noise_object):
sampled_actions = tf.squeeze(actor_model(state))
noise = noise_object()
# Adding noise to action
sampled_actions = sampled_actions.numpy() + noise
# We make sure action is within bounds
legal_action = np.clip(sampled_actions,lower_bound,upper_bound)
return [np.squeeze(legal_action)]
std_dev = 0.2
ou_noise = OUActionNoise(mean=np.zeros(1),std_deviation=float(std_dev) * np.ones(1))
actor_model = get_actor()
critic_model = get_critic()
target_actor = get_actor()
target_critic = get_critic()
# Making the weights equal initially
target_actor.set_weights(actor_model.get_weights())
target_critic.set_weights(critic_model.get_weights())
# Learning rate for actor-critic models
critic_lr = 0.002
actor_lr = 0.001
critic_optimizer = tf.keras.optimizers.Adam(critic_lr)
actor_optimizer = tf.keras.optimizers.Adam(actor_lr)
total_episodes = 100
# discount factor for future rewards
gamma = 0.99
# Used to update target networks
tau = 0.005
buffer = Buffer(50000,64)
# To store reward history of each episode
ep_reward_list = []
# To store average reward history of last few episodes
avg_reward_list = []
# Takes about 4 min to train
for ep in range(total_episodes):
prev_state = env.reset()
episodic_reward = 0
while True:
# Uncomment this to see the Actor in action
# But not in a python notebook.
# env.render()
tf_prev_state = tf.expand_dims(tf.convert_to_tensor(prev_state),0)
action = policy(tf_prev_state,ou_noise)
# Recieve state and reward from environment.
state,done,info = env.step(action)
buffer.record((prev_state,action,state))
episodic_reward += reward
buffer.learn()
update_target(target_actor.variables,actor_model.variables,tau)
update_target(target_critic.variables,critic_model.variables,tau)
# End this episode when `done` is True
if done:
break
prev_state = state
ep_reward_list.append(episodic_reward)
# Mean of last 40 episodes
avg_reward = np.mean(ep_reward_list[-40:])
print("Episode * {} * Avg Reward is ==> {}".format(ep,avg_reward))
avg_reward_list.append(avg_reward)
# Plotting graph
# Episodes versus Avg. Rewards
plt.plot(avg_reward_list)
plt.xlabel("Episode")
plt.ylabel("Avg. Epsiodic Reward")
plt.show()
# Save the weights
actor_model.save_weights("pendulum_actor.h5")
critic_model.save_weights("pendulum_critic.h5")
target_actor.save_weights("pendulum_target_actor.h5")
target_critic.save_weights("pendulum_target_critic.h5")
然后我收到错误：
AttributeError: 'Environment1' 对象没有属性 'observation_space'
有朋友可以帮忙吗？
解决方法
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