深度强化学习代码python

以下是一个简单的深度强化学习代码示例，使用Python编写，使用了Keras和CSDN Gym：

import gym
import random
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

env = gym.make('CartPole-v1')
env.reset()

goal_steps = 500
score_requirement = 60
initial_games = 10000

def model_data_preparation():
    training_data = []
    accepted_scores = []
    for game_index in range(initial_games):
        score = 0
        game_memory = []
        previous_observation = []
        for step_index in range(goal_steps):
            action = random.randrange(0,2)
            observation, reward, done, info = env.step(action)
            if len(previous_observation) > 0:
                game_memory.append([previous_observation, action])
            previous_observation = observation
            score += reward
            if done:
                break
        if score >= score_requirement:
            accepted_scores.append(score)
            for data in game_memory:
                if data[1] == 1:
                    output = [0,1]
                elif data[1] == 0:
                    output = [1,0]
                training_data.append([data[0], output])
        env.reset()
    print(accepted_scores)
    return training_data

def build_model(input_size, output_size):
    model = Sequential()
    model.add(Dense(128, input_dim=input_size, activation='relu'))
    model.add(Dense(52, activation='relu'))
    model.add(Dense(output_size, activation='linear'))
    model.compile(loss='mse', optimizer=Adam())
    return model

def train_model(training_data):
    X = np.array([i[0] for i in training_data]).reshape(-1,len(training_data[0][0]))
    y = np.array([i[1] for i in training_data]).reshape(-1,len(training_data[0][1]))
    model = build_model(input_size=len(X[0]), output_size=len(y[0]))
    model.fit(X, y, epochs=10)
    return model

training_data = model_data_preparation()
trained_model = train_model(training_data)

scores = []
choices = []
for each_game in range(100):
    score = 0
    game_memory = []
    prev_obs = []
    env.reset()
    for _ in range(goal_steps):
        env.render()
        if len(prev_obs)==0:
            action = random.randrange(0,2)
        else:
            action = np.argmax(trained_model.predict(prev_obs.reshape(-1,len(prev_obs)))[0])
        choices.append(action)
        new_observation, reward, done, info = env.step(action)
        prev_obs = new_observation
        game_memory.append([new_observation, action])
        score+=reward
        if done:
            break
    scores.append(score)

print(scores)
print('Average Score:',sum(scores)/len(scores))
print('Choice 1:{}  Choice 0:{}'.format(choices.count(1)/len(choices),choices.count(0)/len(choices)))

这个代码使用CartPole-v1环境，目标是使杆子不掉下来。它使用了一个神经网络，通过学习之前的游戏来预测下一个动作。最后，代码输出了模型在100个游戏中的平均得分和选择每个动作的频率。