OpenAI GYM 아타리게임 학습과 테스트

학습하는 코드는 주석으로 처리 했고 테스트 하는 코드로 있다.

 

출처

https://www.youtube.com/watch?v=hCeJeq8U0lo&t=1801s 

 

 

 

import gym
import random


env = gym.make('SpaceInvaders-v0')
height, width, channels = env.observation_space.shape
actions = env.action_space.n

env.unwrapped.get_action_meanings()

episodes = 5
for episode in range(1, episodes+1):
    state = env.reset()
    done = False
    score = 0

    while not done:
        env.render()
        #action = random.choice([0,1,2,3,4,5])
        action = env.action_space.sample()
        
        n_state, reward, done, info = env.step(action)
        score+=reward
    print('Episode:{} Score:{}'.format(episode, score))
env.close()




import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Convolution2D
from tensorflow.keras.optimizers import Adam
tf.compat.v1.disable_eager_execution()


def build_model(height, width, channels, actions):
    model = Sequential()
    model.add(Convolution2D(32, (8,8), strides=(4,4), activation='relu', input_shape=(3,height, width, channels)))
    model.add(Convolution2D(64, (4,4), strides=(2,2), activation='relu'))
    model.add(Convolution2D(64, (3,3), activation='relu'))
    model.add(Flatten())
    model.add(Dense(512, activation='relu'))
    model.add(Dense(256, activation='relu'))
    model.add(Dense(actions, activation='linear'))
    return model


#del model
model = build_model(height, width, channels, actions)
model.summary()

from rl.agents import DQNAgent
from rl.memory import SequentialMemory
from rl.policy import LinearAnnealedPolicy, EpsGreedyQPolicy

def build_agent(model, actions):
    policy = LinearAnnealedPolicy(EpsGreedyQPolicy(), attr='eps', value_max=1., value_min=.1, value_test=.2, nb_steps=10000)
    memory = SequentialMemory(limit=1000, window_length=3)
    dqn = DQNAgent(model=model, memory=memory, policy=policy, enable_dueling_network=True, dueling_type='avg', nb_actions=actions, nb_steps_warmup=1000)
    return dqn

dqn = build_agent(model, actions)
dqn.compile(Adam(lr=1e-4))

#dqn.fit(env, nb_steps=10000, visualize=False, verbose=2)
#dqn.save_weights('SavedWeights/10k-Fast/dqn_weights.h5f')

dqn.load_weights('SavedWeights/1m/dqn_weights.h5f')
scores = dqn.test(env, nb_episodes=10, visualize=True)
print(np.mean(scores.history['episode_reward']))


del model, dqn