Keras plays catch - a single file Reinforcement Learning example

CATCH_Keras_RL.md

Code for Keras plays catch blog post

Train

python qlearn.py

Test

1. Generate figures

python test.py

2. Make gif

ffmpeg -i %03d.png output.gif -vf fps=1

Alternatively, check cadurosar ipython notebook, there you should run cell 2 before cell 1.

Requirements

• Prior supervised learning and Keras knowledge
• Python science stack (numpy, scipy, matplotlib) - Install Anaconda!
• Theano or Tensorflow
• Keras (last testest on commit b0303f03ff03)
• ffmpeg (optional)

License

This code is released under MIT license. (Note that Deep Q-Learning has its own patent by Google)

qlearn.py

import json
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense
from keras.optimizers import sgd


class Catch(object):
    def __init__(self, grid_size=10):
        self.grid_size = grid_size
        self.reset()

    def _update_state(self, action):
        """
        Input: action and states
        Ouput: new states and reward
        """
        state = self.state
        if action == 0:  # left
            action = -1
        elif action == 1:  # stay
            action = 0
        else:
            action = 1  # right
        f0, f1, basket = state[0]
        new_basket = min(max(1, basket + action), self.grid_size-1)
        f0 += 1
        out = np.asarray([f0, f1, new_basket])
        out = out[np.newaxis]

        assert len(out.shape) == 2
        self.state = out

    def _draw_state(self):
        im_size = (self.grid_size,)*2
        state = self.state[0]
        canvas = np.zeros(im_size)
        canvas[state[0], state[1]] = 1  # draw fruit
        canvas[-1, state[2]-1:state[2] + 2] = 1  # draw basket
        return canvas

    def _get_reward(self):
        fruit_row, fruit_col, basket = self.state[0]
        if fruit_row == self.grid_size-1:
            if abs(fruit_col - basket) <= 1:
                return 1
            else:
                return -1
        else:
            return 0

    def _is_over(self):
        if self.state[0, 0] == self.grid_size-1:
            return True
        else:
            return False

    def observe(self):
        canvas = self._draw_state()
        return canvas.reshape((1, -1))

    def act(self, action):
        self._update_state(action)
        reward = self._get_reward()
        game_over = self._is_over()
        return self.observe(), reward, game_over

    def reset(self):
        n = np.random.randint(0, self.grid_size-1, size=1)
        m = np.random.randint(1, self.grid_size-2, size=1)
        self.state = np.asarray([0, n, m])[np.newaxis]


class ExperienceReplay(object):
    def __init__(self, max_memory=100, discount=.9):
        self.max_memory = max_memory
        self.memory = list()
        self.discount = discount

    def remember(self, states, game_over):
        # memory[i] = [[state_t, action_t, reward_t, state_t+1], game_over?]
        self.memory.append([states, game_over])
        if len(self.memory) > self.max_memory:
            del self.memory[0]

    def get_batch(self, model, batch_size=10):
        len_memory = len(self.memory)
        num_actions = model.output_shape[-1]
        env_dim = self.memory[0][0][0].shape[1]
        inputs = np.zeros((min(len_memory, batch_size), env_dim))
        targets = np.zeros((inputs.shape[0], num_actions))
        for i, idx in enumerate(np.random.randint(0, len_memory,
                                                  size=inputs.shape[0])):
            state_t, action_t, reward_t, state_tp1 = self.memory[idx][0]
            game_over = self.memory[idx][1]

            inputs[i:i+1] = state_t
            # There should be no target values for actions not taken.
            # Thou shalt not correct actions not taken #deep
            targets[i] = model.predict(state_t)[0]
            Q_sa = np.max(model.predict(state_tp1)[0])
            if game_over:  # if game_over is True
                targets[i, action_t] = reward_t
            else:
                # reward_t + gamma * max_a' Q(s', a')
                targets[i, action_t] = reward_t + self.discount * Q_sa
        return inputs, targets


if __name__ == "__main__":
    # parameters
    epsilon = .1  # exploration
    num_actions = 3  # [move_left, stay, move_right]
    epoch = 1000
    max_memory = 500
    hidden_size = 100
    batch_size = 50
    grid_size = 10

    model = Sequential()
    model.add(Dense(hidden_size, input_shape=(grid_size**2,), activation='relu'))
    model.add(Dense(hidden_size, activation='relu'))
    model.add(Dense(num_actions))
    model.compile(sgd(lr=.2), "mse")

    # If you want to continue training from a previous model, just uncomment the line bellow
    # model.load_weights("model.h5")

    # Define environment/game
    env = Catch(grid_size)

    # Initialize experience replay object
    exp_replay = ExperienceReplay(max_memory=max_memory)

    # Train
    win_cnt = 0
    for e in range(epoch):
        loss = 0.
        env.reset()
        game_over = False
        # get initial input
        input_t = env.observe()

        while not game_over:
            input_tm1 = input_t
            # get next action
            if np.random.rand() <= epsilon:
                action = np.random.randint(0, num_actions, size=1)
            else:
                q = model.predict(input_tm1)
                action = np.argmax(q[0])

            # apply action, get rewards and new state
            input_t, reward, game_over = env.act(action)
            if reward == 1:
                win_cnt += 1

            # store experience
            exp_replay.remember([input_tm1, action, reward, input_t], game_over)

            # adapt model
            inputs, targets = exp_replay.get_batch(model, batch_size=batch_size)

            loss += model.train_on_batch(inputs, targets)[0]
        print("Epoch {:03d}/999 | Loss {:.4f} | Win count {}".format(e, loss, win_cnt))

    # Save trained model weights and architecture, this will be used by the visualization code
    model.save_weights("model.h5", overwrite=True)
    with open("model.json", "w") as outfile:
        json.dump(model.to_json(), outfile)

test.py

import json
import matplotlib.pyplot as plt
import numpy as np
from keras.models import model_from_json
from qlearn import Catch


if __name__ == "__main__":
    # Make sure this grid size matches the value used fro training
    grid_size = 10

    with open("model.json", "r") as jfile:
        model = model_from_json(json.load(jfile))
    model.load_weights("model.h5")
    model.compile("sgd", "mse")

    # Define environment, game
    env = Catch(grid_size)
    c = 0
    for e in range(10):
        loss = 0.
        env.reset()
        game_over = False
        # get initial input
        input_t = env.observe()

        plt.imshow(input_t.reshape((grid_size,)*2),
                   interpolation='none', cmap='gray')
        plt.savefig("%03d.png" % c)
        c += 1
        while not game_over:
            input_tm1 = input_t

            # get next action
            q = model.predict(input_tm1)
            action = np.argmax(q[0])

            # apply action, get rewards and new state
            input_t, reward, game_over = env.act(action)

            plt.imshow(input_t.reshape((grid_size,)*2),
                       interpolation='none', cmap='gray')
            plt.savefig("%03d.png" % c)
            c += 1

How can I incrementally fine tune a trained model if I change the the falling speed or some other parameters of the game slightly?

@qrpike what was the solution of your problem? You should write answer instead 'say thanks'!

I've created a Paddle Ball game using Deep Q Learning based on this work. Thanks Eder for the basics! It is really helpful for a biginner like me.
https://github.com/azhar2205/paddle-ball-using-dqlearn

Where does one get the code for these simple games like catch ?
Thank you

@NaderNazemi
You may want to check out http://karpathy.github.io/2016/05/31/rl/ and also https://gym.openai.com/

@EderSantana thanks for the great and informative project!
@cadurosar thank you for contributing the notebook version!
I made a version with more comments and explanations for teaching purposes. It can be run cell by cell like a walk through tutorial. It runs in python 2.7 as well as 3.5 and fixes some of the errors mentioned in the comments above. You can find it here: https://github.com/JannesKlaas/sometimes_deep_sometimes_learning/blob/master/reinforcement.ipynb

Thank you @Sohojoe commented on May 24, 2016 • edited
That worked for me, I am using Python too!

Thanks for the comment @arr28

I did not understand the code. Could you please explain it line by line? That will be of great help.

thanks, very nice code !

Hello, Eder

Thanks for the nice reinforcement example. I could study about reinforcement learning efficiently.

I'm not good at English, but I hope it's understable to you.

By the way, I have an Idea for more good train. I think the basket should wait under the fruit before it get fall to the ground. So I changed definition of _get_reward() like this.

def _get_reward_2nd(self):
        fruit_row, fruit_col, basket = self.state[0]
        if abs(fruit_col - basket) <= 1:
            return 1
        else:
            return -1

After changing definition, the win_cnt soared like this,

Epoch 996/999 | Loss 0.0043 | Win count 7166
Epoch 997/999 | Loss 0.0051 | Win count 7173
Epoch 998/999 | Loss 0.0048 | Win count 7182
Epoch 999/999 | Loss 0.0058 | Win count 7186

I'm sorry if you have already know this. I hope my opinion would be your help.

I think there might be some misunderstanding of your code. You get more count because you will get reward when the fruit has not reached the last row but near to the basket in column. But it doesn't mean the code has got the better training. But I am a beginner too and I have many question of the code. So maybe I am wrong. I just hope it will help you to get better. Thank you.

awesome! thanks for letting me know @upstarsong

I did not understand the code. Could you please explain it line by line? That will be of great help.

Agreed, I am looking at the code not understanding the majority of the functionality. I'm glad it has helped some users but I'm relatively new to the subject and without additional comments there little I could hope to learn from this example.