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DDQN_PER_faster.py
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DDQN_PER_faster.py
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#PER
import random
import gym
import math
import numpy as np
from collections import deque
import heapq
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import itertools
score_log = []
loss_log = []
tiebreaker = itertools.count()
class DDQNPERSolver():
def __init__(self, avg_target=475, gamma=1.0, batch_size=64):
self.learning_rate = 0.01
self.learning_rate_decay = 0.01
self.epsilon = 1.0
self.epsilon_min = 0.01
self.epsilon_decay = 0.995
self.memory = []
self.env = gym.make('CartPole-v1')
self.n_episodes = 100000000
self.env._max_episode_steps = None
self.avg_target = avg_target
self.batch_size = batch_size
self.gamma = gamma
self.model=self.build_model(self.learning_rate, self.learning_rate_decay)
self.target_model = self.build_model(self.learning_rate, self.learning_rate_decay)
self.update_target_model()
#graphic properties
plt.rcParams['image.cmap'] = 'RdYlGn'
plt.rcParams['figure.figsize'] = [15.0, 6.0]
plt.rcParams['figure.dpi'] = 80
plt.rcParams['savefig.dpi'] = 30
def build_model(self, learning_rate, learning_rate_decay):
model = Sequential()
model.add(Dense(24, input_dim=4, activation='tanh'))
model.add(Dense(48, activation='tanh'))
model.add(Dense(48, activation='tanh'))
model.add(Dense(2, activation='linear'))
model.compile(loss='mse', optimizer=Adam(lr=learning_rate, decay=learning_rate_decay))
return model
def remember(self, state, action, reward, next_state, done):
predicted_q_value = self.model.predict(next_state)[0]
action_next_best = np.argmax(predicted_q_value)
predicted_q_value_target = self.target_model.predict(next_state)[0]
current_score = reward + self.gamma * np.max(self.target_model.predict(next_state)[0])
td_error = abs(current_score - predicted_q_value)
current_action = (td_error,state,action,reward, next_state, done)
if len(self.memory) <= 10000000000000:
heapq.heappush(self.memory,(next(tiebreaker),current_action))
else:
self.memory[0] = current_action
heapq.heapify(self.memory)
def choose_action(self, state, epsilon):
return self.env.action_space.sample() if (np.random.random() <= epsilon) else np.argmax(self.model.predict(state))
def get_epsilon(self, t):
return max(self.epsilon_min, min(self.epsilon, 1.0 - math.log10((t + 1) * self.epsilon_decay)))
def preprocess_state(self, state):
return np.reshape(state, [1, 4])
def replay(self, batch_size):
states, targets = [], []
current_batch = heapq.nlargest(batch_size, self.memory)
for _, current_action in current_batch:
td, state, action, reward, next_state, done = current_action
predicted_target = self.model.predict(state)
if done:
predicted_target[0][action] = reward
else:
predicted_target[0][action] = reward + self.gamma * np.max(self.target_model.predict(next_state)[0])
states.append(state[0])
targets.append(predicted_target[0])
history = self.model.fit(np.array(states), np.array(targets), batch_size=len(states), verbose=0)
loss = history.history['loss'][0]
if self.epsilon > self.epsilon_min:
self.epsilon = self.epsilon * self.epsilon_decay
return loss
def update_target_model(self):
self.target_model.set_weights(self.model.get_weights())
def print_graphs(self):
plt.plot(range(len(score_log)), score_log[0:(len(score_log))] , 'o')
plt.title("Total reward per episode")
plt.show()
plt.plot(range(len(loss_log)), loss_log , 'o')
plt.title("Total loss per episode")
plt.show()
avg_list = []
for r in range(len(score_log)+1):
if r > 99:
avg_list.append(np.mean(score_log[(r - 100): r]))
else:
avg_list.append(np.mean(score_log[(0): r]))
plt.plot(range(len(avg_list)), avg_list , '-')
plt.title("AVG reward per 100 episode")
plt.show()
def solveProblem(self):
scores = deque(maxlen=100)
for episode in range(self.n_episodes):
state = self.preprocess_state(self.env.reset())
done = False
total_reward = 0
while not done and total_reward < 10000:
action = self.choose_action(state, self.get_epsilon(episode))
next_state, reward, done, _ = self.env.step(action)
next_state = self.preprocess_state(next_state)
self.remember(state, action, reward, next_state, done)
state = next_state
total_reward += 1
scores.append(total_reward)
score_log.append(total_reward)
avg_score = np.mean(scores)
if avg_score >= self.avg_target and episode >= 100:
print('Solved after: {} episodes with AVG of {}.'.format(episode, avg_score))
return episode
if episode % 100 == 0:
print('AVG score: {} after {} episodes.'.format(avg_score, episode))
if total_reward > 500:
print('Episode: {} got score of {} to the current avg of {}'.format(episode, total_reward, avg_score))
self.target_model.set_weights(self.model.get_weights())
loss = self.replay(self.batch_size)
loss_log.append(loss)
return e
if __name__ == '__main__':
solver = DDQNPERSolver(avg_target=475, gamma=1.0, batch_size=64)
solver.solveProblem()
solver.print_graphs()