Deep Q-Learning Network with Keras and OpenAI Gym, based on Keon Kim's code.
In [1]:
# import os
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
# os.environ["CUDA_VISIBLE_DEVICES"] = ""
# os.environ["CUDA_VISIBLE_DEVICES"] = "1"
In [2]:
import random
import gym
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
import os # for creating directories
Using TensorFlow backend.
In [3]:
env = gym.make('CartPole-v0') # initialise environment
In [4]:
state_size = env.observation_space.shape[0]
state_size
Out[4]:
4
In [5]:
action_size = env.action_space.n
action_size
Out[5]:
2
In [6]:
batch_size = 32
In [7]:
n_episodes = 1001 # n games we want agent to play (default 1001)
In [8]:
output_dir = 'model_output/cartpole/'
In [9]:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
In [10]:
class DQNAgent:
def __init__(self, state_size, action_size):
self.state_size = state_size
self.action_size = action_size
self.memory = deque(maxlen=2000) # double-ended queue; acts like list, but elements can be added/removed from either end
self.gamma = 0.95 # decay or discount rate: enables agent to take into account future actions in addition to the immediate ones, but discounted at this rate
self.epsilon = 1.0 # exploration rate: how much to act randomly; more initially than later due to epsilon decay
self.epsilon_decay = 0.995 # decrease number of random explorations as the agent's performance (hopefully) improves over time
self.epsilon_min = 0.01 # minimum amount of random exploration permitted
self.learning_rate = 0.001 # rate at which NN adjusts models parameters via SGD to reduce cost
self.model = self._build_model() # private method
def _build_model(self):
# neural net to approximate Q-value function:
model = Sequential()
model.add(Dense(24, input_dim=self.state_size, activation='relu')) # 1st hidden layer; states as input
model.add(Dense(24, activation='relu')) # 2nd hidden layer
model.add(Dense(self.action_size, activation='linear')) # 2 actions, so 2 output neurons: 0 and 1 (L/R)
model.compile(loss='mse',
optimizer=Adam(lr=self.learning_rate))
return model
def remember(self, state, action, reward, next_state, done):
self.memory.append((state, action, reward, next_state, done)) # list of previous experiences, enabling re-training later
def act(self, state):
if np.random.rand() <= self.epsilon: # if acting randomly, take random action
return random.randrange(self.action_size)
act_values = self.model.predict(state) # if not acting randomly, predict reward value based on current state
return np.argmax(act_values[0]) # pick the action that will give the highest reward (i.e., go left or right?)
def replay(self, batch_size): # method that trains NN with experiences sampled from memory
minibatch = random.sample(self.memory, batch_size) # sample a minibatch from memory
for state, action, reward, next_state, done in minibatch: # extract data for each minibatch sample
target = reward # if done (boolean whether game ended or not, i.e., whether final state or not), then target = reward
if not done: # if not done, then predict future discounted reward
target = (reward + self.gamma * # (target) = reward + (discount rate gamma) *
np.amax(self.model.predict(next_state)[0])) # (maximum target Q based on future action a')
target_f = self.model.predict(state) # approximately map current state to future discounted reward
target_f[0][action] = target
self.model.fit(state, target_f, epochs=1, verbose=0) # single epoch of training with x=state, y=target_f; fit decreases loss btwn target_f and y_hat
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
def load(self, name):
self.model.load_weights(name)
def save(self, name):
self.model.save_weights(name)
In [11]:
agent = DQNAgent(state_size, action_size) # initialise agent
In [12]:
done = False
for e in range(n_episodes): # iterate over new episodes of the game
state = env.reset() # reset state at start of each new episode of the game
state = np.reshape(state, [1, state_size])
for time in range(5000): # time represents a frame of the game; goal is to keep pole upright as long as possible up to range, e.g., 500 or 5000 timesteps
# env.render()
action = agent.act(state) # action is either 0 or 1 (move cart left or right); decide on one or other here
next_state, reward, done, _ = env.step(action) # agent interacts with env, gets feedback; 4 state data points, e.g., pole angle, cart position
reward = reward if not done else -10 # reward +1 for each additional frame with pole upright
next_state = np.reshape(next_state, [1, state_size])
agent.remember(state, action, reward, next_state, done) # remember the previous timestep's state, actions, reward, etc.
state = next_state # set "current state" for upcoming iteration to the current next state
if done: # episode ends if agent drops pole or we reach timestep 5000
print("episode: {}/{}, score: {}, e: {:.2}" # print the episode's score and agent's epsilon
.format(e, n_episodes, time, agent.epsilon))
break # exit loop
if len(agent.memory) > batch_size:
agent.replay(batch_size) # train the agent by replaying the experiences of the episode
if e % 50 == 0:
agent.save(output_dir + "weights_" + '{:04d}'.format(e) + ".hdf5")
episode: 0/1001, score: 8, e: 1.0
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In [13]:
# saved agents can be loaded with agent.load("./path/filename.hdf5")
Content source: the-deep-learners/TensorFlow-LiveLessons
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