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# Inspired by https://keon.io/deep-q-learning/ | |
import random | |
import gym | |
import math | |
import numpy as np | |
from collections import deque | |
from keras.models import Sequential | |
from keras.layers import Dense | |
from keras.optimizers import Adam | |
class DQNCartPoleSolver(): | |
def __init__(self, n_episodes=1000, n_win_ticks=195, max_env_steps=None, gamma=1.0, epsilon=1.0, epsilon_min=0.01, epsilon_log_decay=0.995, alpha=0.01, alpha_decay=0.01, batch_size=64, monitor=False, quiet=False): | |
self.memory = deque(maxlen=100000) | |
self.env = gym.make('CartPole-v0') | |
if monitor: self.env = gym.wrappers.Monitor(self.env, '../data/cartpole-1', force=True) | |
self.gamma = gamma | |
self.epsilon = epsilon | |
self.epsilon_min = epsilon_min | |
self.epsilon_decay = epsilon_log_decay | |
self.alpha = alpha | |
self.alpha_decay = alpha_decay | |
self.n_episodes = n_episodes | |
self.n_win_ticks = n_win_ticks | |
self.batch_size = batch_size | |
self.quiet = quiet | |
if max_env_steps is not None: self.env._max_episode_steps = max_env_steps | |
# Init model | |
self.model = Sequential() | |
self.model.add(Dense(24, input_dim=4, activation='tanh')) | |
self.model.add(Dense(48, activation='tanh')) | |
self.model.add(Dense(2, activation='linear')) | |
self.model.compile(loss='mse', optimizer=Adam(lr=self.alpha, decay=self.alpha_decay)) | |
def remember(self, state, action, reward, next_state, done): | |
self.memory.append((state, action, reward, next_state, done)) | |
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): | |
x_batch, y_batch = [], [] | |
minibatch = random.sample( | |
self.memory, min(len(self.memory), batch_size)) | |
for state, action, reward, next_state, done in minibatch: | |
y_target = self.model.predict(state) | |
y_target[0][action] = reward if done else reward + self.gamma * np.max(self.model.predict(next_state)[0]) | |
x_batch.append(state[0]) | |
y_batch.append(y_target[0]) | |
self.model.fit(np.array(x_batch), np.array(y_batch), batch_size=len(x_batch), verbose=0) | |
if self.epsilon > self.epsilon_min: | |
self.epsilon *= self.epsilon_decay | |
def run(self): | |
scores = deque(maxlen=100) | |
for e in range(self.n_episodes): | |
state = self.preprocess_state(self.env.reset()) | |
done = False | |
i = 0 | |
while not done: | |
action = self.choose_action(state, self.get_epsilon(e)) | |
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 | |
i += 1 | |
scores.append(i) | |
mean_score = np.mean(scores) | |
if mean_score >= self.n_win_ticks and e >= 100: | |
if not self.quiet: print('Ran {} episodes. Solved after {} trials ✔'.format(e, e - 100)) | |
return e - 100 | |
if e % 100 == 0 and not self.quiet: | |
print('[Episode {}] - Mean survival time over last 100 episodes was {} ticks.'.format(e, mean_score)) | |
self.replay(self.batch_size) | |
if not self.quiet: print('Did not solve after {} episodes 😞'.format(e)) | |
return e | |
if __name__ == '__main__': | |
agent = DQNCartPoleSolver() | |
agent.run() |
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@SanDramaa Yes, that’s how deque works. It can only hold max length of 100 specified.