ラズパイでkeras その2

概要

ラズパイでkerasやってみた。
gymのcartpoleやってみた。
kerasで強化学習やってみた。

写真

サンプルコード

import gym
import numpy as np
import random
from tensorflow.contrib.keras.python.keras.models import Model
from tensorflow.contrib.keras.python.keras.layers import *
from tensorflow.contrib.keras.python.keras import backend as K
from collections import deque
import matplotlib.pyplot as plt

def randf(s, e):
    return (float(random.randrange(0, (e - s) * 9999)) / 10000) + s;

def discount_rewards(r, gamma = 0.99):
    discounted_r = np.zeros_like(r)
    running_add = 0
    for t in reversed(range(0, len(r))):
        running_add = running_add * gamma + r[t]
        discounted_r[t] = running_add
    return discounted_r

def one_hot(index, categories):
    x = np.zeros((categories, ))
    x[index] = 1
    return x

def loss(advan):
    def f(y_true, y_pred):
        respons = K.sum(y_true * y_pred, axis = 1)
        policy_loss = -K.sum(advan * K.log(respons))
        return policy_loss
    return f

env = gym.make('CartPole-v0')
state = Input(shape = (4, ))
x = Dense(24)(state)
x = Dense(24)(x)
x = Dense(2)(x)
x = Activation('softmax')(x)
model_a = Model(state, x)
state = Input(shape = (4, ))
adv_input = Input(shape = (1, ))
x = model_a(state)
model_b = Model([state, adv_input], x)
model_b.compile(optimizer = 'adam', loss = loss(adv_input)) 
all_rewards = deque(maxlen = 100)
epsilon = 1
epsilonMinimumValue = 0.001
reward_trend = []
for i_episode in range(200):
    observation = env.reset()
    state_history, action_history, reward_history = [], [], []
    for t in range(200):
        env.render()
        state_history.append(observation)
        pred = model_a.predict(np.expand_dims(observation, axis = 0))[0]
        if (randf(0, 1) < epsilon):
            action = np.random.choice(len(pred), 1, p = pred)[0]
        else:
            action = np.argmax(pred)
        if (epsilon > epsilonMinimumValue):
            epsilon = epsilon * 0.999
        observation, reward, done, info = env.step(action)
        reward_history.append(reward)
        action_history.append(one_hot(action, 2))
        if done:
            reward_sum = sum(reward_history)
            reward_trend.append(reward_sum)
            print (i_episode, "Episode finished with reward {} {:.2f}".format(reward_sum, np.mean(all_rewards)))
            if reward_sum > 198:
                reward_sum += 100
            all_rewards.append(reward_sum)
            adv = discount_rewards(reward_history)
            state_history = np.array(state_history)
            action_history = np.array(action_history)
            model_b.train_on_batch([state_history, adv], action_history)
            break
plt.plot(reward_trend)
plt.savefig("gym26.png")
plt.show()

結果

以上。