I have implemented a DQN using keras. The task is to collect the circles and avoid the red circle and crosses. The associated rewards are +5, -5 and 0 otherwise. if the agent go out of the board, the game is reset (reward -5 too).

The average reward fluctuates a long and I cannot observe any learning. I tried to use similar settings as for DQN Atari except that I don't concatenate the last 4 frames but train the neural network on the RGB image.

Is there any bug in my code, or do you have any idea how to learn from this environment?

My code:

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,RMSprop

import sys
from snake_env import SnakeEnv
from keras import models
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Flatten
from keras.models import Model
import matplotlib.pyplot as plt
import cv2

class DQNAgent:
    def __init__(self, state_size, action_size):
    self.state_size = state_size
    self.action_size = action_size
    #number of pretrrainign steps
    self.init_learn = 100
    self.epsilon_decay = 100000
    self.memory = deque(maxlen=self.epsilon_decay)
    self.gamma = 0.95    # discount rate
    self.epsilon = 1.0  # exploration rate
    self.epsilon_min = 0.01
    #self.epsilon_decay = 0.995
    self.learning_rate = 0.00025
    self.model = self._build_model()
    self.epsilon_rate = ((self.epsilon - self.epsilon_min) /self.epsilon_decay)

    def _build_model(self):
        model = models.Sequential()
        #input_img = Input(shape=(84,84,3))
        model.add(Conv2D(32, (8, 8), strides=(4, 4), activation='relu',
        model.add(Conv2D(64, (4, 4), strides=(2, 2), activation='relu'))
        model.add(Conv2D(64, (3, 3), strides=(1, 1), activation='relu'))
        model.add(Dense(512, activation='relu'))
        #model = Model(input_img, x)
        return model

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        act_values = self.model.predict(np.array([state]))
        return np.argmax(act_values[0])  # returns action

    def replay(self, batch_size):
        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = (reward + self.gamma *
                target_f = self.model.predict(np.array([state]))
            target_f[0][action] = target
            self.model.fit(np.array([state]), target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon = max(self.epsilon_min,self.epsilon - self.epsilon_rate)

    def load(self, name):

    def save(self, name):

def processor(image):
    return cv2.resize(image,(84,84),interpolation=cv2.INTER_NEAREST)

def running_mean(x, N):
    cumsum = np.cumsum(np.insert(x, 0, 0))
    return (cumsum[N:] - cumsum[:-N]) / float(N)

def main():
    env = SnakeEnv()
    state_size = env.observation_space.shape[0]
    action_size = env.action_space.n
    agent = DQNAgent(state_size, action_size)
    batch_size = 32
    graphics = True
    if graphics:
    fig = plt.figure(figsize=(8, 4))
    ax1 = plt.subplot(1, 2, 1)
    ax2 = plt.subplot(1, 2, 2)
    rewards= []
    steps = []
    step_total =0
    for ep in range(500000):
    #reset environnment

    state = env.render()
    state = processor(state)
    reward_cuml = 0
    step = 0
    while True:
        step +=1
        step_total +=1
        #take a step
        action = agent.act(state)
        next_state, reward, done, _ = env.step(action)
        reward_cuml += reward
        next_state = processor(next_state)
        if done:
            print("Number of steps ", step_total ," Epsilon ",agent.epsilon)
            print("Episode ", ep , " reward " , np.sum(rewards) , " mean reward " , np.mean(rewards))
            if graphics:

                ax1.plot(running_mean(rewards,10), '.--')
                ax2.plot(running_mean(steps,10), '.--')
                if ep%50==0:

        state = next_state
        if len(agent.memory) > agent.init_learn:

how looks like the environment:

enter image description here

Learning curve (average reward / episode)

enter image description here


migrated from ai.stackexchange.com Oct 6 '18 at 16:39

This question came from our site for people interested in conceptual questions about life and challenges in a world where "cognitive" functions can be mimicked in purely digital environment.

  • 1
    $\begingroup$ You have some indentation problems in your code. You will need to sort them out to match your original if you hope someone will read the code. Although there is a lot of code here. Is snake_env a public library, or your own creation? If your own, it becomes harder to analyse or replicate your results. $\endgroup$ – Neil Slater Sep 27 '18 at 9:48
  • $\begingroup$ @NeilSlater I fixed the identation problems, this environment is my own creation. Do you see any problems in my code ? $\endgroup$ – Nicolas Sep 28 '18 at 0:48
  • $\begingroup$ Please double-check the indentation on line target_f = self.model.predict(np.array([state])) - it seems wrong to me. $\endgroup$ – Neil Slater Sep 28 '18 at 9:23
  • $\begingroup$ If it were possible, I would try the algorithm on a 2x2 or a 3x3 board with only a few elements (1 red square, 1 blue circle), then try to check manually whether the q-factors make sense in obvious situations. Like if there is a red square in the position above, then moving up should have a very low value etc. $\endgroup$ – Hai Nguyen Sep 28 '18 at 10:06
  • 1
    $\begingroup$ I have to say I disagree with the migration here, and have raised a meta about it - ai.meta.stackexchange.com/questions/1448/… - however, that is not too important for getting an answer, there are plenty of RL experts here in DataScience and also in CrossValidated. $\endgroup$ – Neil Slater Oct 6 '18 at 21:07

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