Advanced Deep Learning with Keras

Deep Reinforcement Learning
from keras.models import Model
from keras.optimizers import Adam
from collections import deque
import numpy as np
import random
import argparse
import gym
from gym import wrappers, logger
class DQNAgent():
def __init__(self, state_space, action_space, args,
episodes=1000):
self.action_space = action_space
# experience buffer
self.memory = []
# discount rate
self.gamma = 0.9
# initially 90% exploration, 10% exploitation
self.epsilon = 0.9
# iteratively applying decay til 10% exploration/90%
exploitation
self.epsilon_min = 0.1
self.epsilon_decay = self.epsilon_min / self.epsilon
self.epsilon_decay = self.epsilon_decay ** (1. /
float(episodes))
# Q Network weights filename
self.weights_file = 'dqn_cartpole.h5'
# Q Network for training
n_inputs = state_space.shape[0]
n_outputs = action_space.n
self.q_model = self.build_model(n_inputs, n_outputs)
self.q_model.compile(loss='mse', optimizer=Adam())
# target Q Network
self.target_q_model = self.build_model(n_inputs, n_outputs)
# copy Q Network params to target Q Network
self.update_weights()
self.replay_counter = 0
self.ddqn = True if args.ddqn else False
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