» Code examples / Reinforcement learning / Deep Q-Learning for Atari Breakout

Deep Q-Learning for Atari Breakout

Author: Jacob Chapman and Mathias Lechner
Date created: 2020/05/23
Last modified: 2020/06/17
Description: Play Atari Breakout with a Deep Q-Network.

View in Colab GitHub source


This script shows an implementation of Deep Q-Learning on the BreakoutNoFrameskip-v4 environment.

Deep Q-Learning

As an agent takes actions and moves through an environment, it learns to map the observed state of the environment to an action. An agent will choose an action in a given state based on a "Q-value", which is a weighted reward based on the expected highest long-term reward. A Q-Learning Agent learns to perform its task such that the recommended action maximizes the potential future rewards. This method is considered an "Off-Policy" method, meaning its Q values are updated assuming that the best action was chosen, even if the best action was not chosen.

Atari Breakout

In this environment, a board moves along the bottom of the screen returning a ball that will destroy blocks at the top of the screen. The aim of the game is to remove all blocks and breakout of the level. The agent must learn to control the board by moving left and right, returning the ball and removing all the blocks without the ball passing the board.


The Deepmind paper trained for "a total of 50 million frames (that is, around 38 days of game experience in total)". However this script will give good results at around 10 million frames which are processed in less than 24 hours on a modern machine.



from baselines.common.atari_wrappers import make_atari, wrap_deepmind
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# Configuration paramaters for the whole setup
seed = 42
gamma = 0.99  # Discount factor for past rewards
epsilon = 1.0  # Epsilon greedy parameter
epsilon_min = 0.1  # Minimum epsilon greedy parameter
epsilon_max = 1.0  # Maximum epsilon greedy parameter
epsilon_interval = (
    epsilon_max - epsilon_min
)  # Rate at which to reduce chance of random action being taken
batch_size = 32  # Size of batch taken from replay buffer
max_steps_per_episode = 10000

# Use the Baseline Atari environment because of Deepmind helper functions
env = make_atari("BreakoutNoFrameskip-v4")
# Warp the frames, grey scale, stake four frame and scale to smaller ratio
env = wrap_deepmind(env, frame_stack=True, scale=True)

Implement the Deep Q-Network

This network learns an approximation of the Q-table, which is a mapping between the states and actions that an agent will take. For every state we'll have four actions, that can be taken. The environment provides the state, and the action is chosen by selecting the larger of the four Q-values predicted in the output layer.

num_actions = 4

def create_q_model():
    # Network defined by the Deepmind paper
    inputs = layers.Input(shape=(84, 84, 4,))

    # Convolutions on the frames on the screen
    layer1 = layers.Conv2D(32, 8, strides=4, activation="relu")(inputs)
    layer2 = layers.Conv2D(64, 4, strides=2, activation="relu")(layer1)
    layer3 = layers.Conv2D(64, 3, strides=1, activation="relu")(layer2)

    layer4 = layers.Flatten()(layer3)

    layer5 = layers.Dense(512, activation="relu")(layer4)
    action = layers.Dense(num_actions, activation="linear")(layer5)

    return keras.Model(inputs=inputs, outputs=action)

# The first model makes the predictions for Q-values which are used to
# make a action.
model = create_q_model()
# Build a target model for the prediction of future rewards.
# The weights of a target model get updated every 10000 steps thus when the
# loss between the Q-values is calculated the target Q-value is stable.
model_target = create_q_model()


# In the Deepmind paper they use RMSProp however then Adam optimizer
# improves training time
optimizer = keras.optimizers.Adam(learning_rate=0.00025, clipnorm=1.0)

# Experience replay buffers
action_history = []
state_history = []
state_next_history = []
rewards_history = []
done_history = []
episode_reward_history = []
running_reward = 0
episode_count = 0
frame_count = 0
# Number of frames to take random action and observe output
epsilon_random_frames = 50000
# Number of frames for exploration
epsilon_greedy_frames = 1000000.0
# Maximum replay length
# Note: The Deepmind paper suggests 1000000 however this causes memory issues
max_memory_length = 100000
# Train the model after 4 actions
update_after_actions = 4
# How often to update the target network
update_target_network = 10000
# Using huber loss for stability
loss_function = keras.losses.Huber()

while True:  # Run until solved
    state = np.array(env.reset())
    episode_reward = 0

    for timestep in range(1, max_steps_per_episode):
        # env.render(); Adding this line would show the attempts
        # of the agent in a pop up window.
        frame_count += 1

        # Use epsilon-greedy for exploration
        if frame_count < epsilon_random_frames or epsilon > np.random.rand(1)[0]:
            # Take random action
            action = np.random.choice(num_actions)
            # Predict action Q-values
            # From environment state
            state_tensor = tf.convert_to_tensor(state)
            state_tensor = tf.expand_dims(state_tensor, 0)
            action_probs = model(state_tensor, training=False)
            # Take best action
            action = tf.argmax(action_probs[0]).numpy()

        # Decay probability of taking random action
        epsilon -= epsilon_interval / epsilon_greedy_frames
        epsilon = max(epsilon, epsilon_min)

        # Apply the sampled action in our environment
        state_next, reward, done, _ = env.step(action)
        state_next = np.array(state_next)

        episode_reward += reward

        # Save actions and states in replay buffer
        state = state_next

        # Update every fourth frame and once batch size is over 32
        if frame_count % update_after_actions == 0 and len(done_history) > batch_size:

            # Get indices of samples for replay buffers
            indices = np.random.choice(range(len(done_history)), size=batch_size)

            # Using list comprehension to sample from replay buffer
            state_sample = np.array([state_history[i] for i in indices])
            state_next_sample = np.array([state_next_history[i] for i in indices])
            rewards_sample = [rewards_history[i] for i in indices]
            action_sample = [action_history[i] for i in indices]
            done_sample = tf.convert_to_tensor(
                [float(done_history[i]) for i in indices]

            # Build the updated Q-values for the sampled future states
            # Use the target model for stability
            future_rewards = model_target.predict(state_next_sample)
            # Q value = reward + discount factor * expected future reward
            updated_q_values = rewards_sample + gamma * tf.reduce_max(
                future_rewards, axis=1

            # If final frame set the last value to -1
            updated_q_values = updated_q_values * (1 - done_sample) - done_sample

            # Create a mask so we only calculate loss on the updated Q-values
            masks = tf.one_hot(action_sample, num_actions)

            with tf.GradientTape() as tape:
                # Train the model on the states and updated Q-values
                q_values = model(state_sample)

                # Apply the masks to the Q-values to get the Q-value for action taken
                q_action = tf.reduce_sum(tf.multiply(q_values, masks), axis=1)
                # Calculate loss between new Q-value and old Q-value
                loss = loss_function(updated_q_values, q_action)

            # Backpropagation
            grads = tape.gradient(loss, model.trainable_variables)
            optimizer.apply_gradients(zip(grads, model.trainable_variables))

        if frame_count % update_target_network == 0:
            # update the the target network with new weights
            # Log details
            template = "running reward: {:.2f} at episode {}, frame count {}"
            print(template.format(running_reward, episode_count, frame_count))

        # Limit the state and reward history
        if len(rewards_history) > max_memory_length:
            del rewards_history[:1]
            del state_history[:1]
            del state_next_history[:1]
            del action_history[:1]
            del done_history[:1]

        if done:

    # Update running reward to check condition for solving
    if len(episode_reward_history) > 100:
        del episode_reward_history[:1]
    running_reward = np.mean(episode_reward_history)

    episode_count += 1

    if running_reward > 40:  # Condition to consider the task solved
        print("Solved at episode {}!".format(episode_count))


Before any training: Imgur

In early stages of training: Imgur

In later stages of training: Imgur