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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() |
In the code it says: 'Mean survival time over last 100 episodes was ...', however I see in the code that the mean is taken over all the scores. Is that correct?
Or is that how deque works?
@SanDramaa Yes, that’s how deque works. It can only hold max length of 100 specified.
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Perhaps they need to run it 5, or even 30 times and only use the mean number of episodes that it taeks to solve. This would give an estimate that was an overestimate about half the time and an under-estimate about half the time. An even better way to do scoring would be to run it 300 times, and report the 5th and 95th percentiles of the number of epochs to solve along with the mean. One would expect performance outside of the window only about 10% of the time. If you look at tasks like visual feature recognition the evaluation is now in the 1% or the 5%, so the extremal score is meaningful when the expectation is not. As ML and AI get better I would expect more distance into extremal as measures of performance.