This blog teaches you how to use TensorFlow to implement reinforcement learning, a branch of machine learning that deals with learning from actions and rewards. You will also see some examples of RL applied to game playing problems.

## 1. Introduction to Reinforcement Learning

**Reinforcement learning** (RL) is a branch of machine learning that deals with learning from actions and rewards. In RL, an **agent** interacts with an **environment** and learns how to achieve a goal by trial and error. The agent receives a **reward** for each action it takes, and tries to maximize the total reward over time.

RL is different from other types of machine learning, such as supervised learning and unsupervised learning, in several ways. For example:

- In RL, the agent does not have access to labeled data or explicit feedback. It has to learn from its own experience and exploration.
- In RL, the agent’s actions affect the environment and the future state. The agent has to deal with the consequences of its actions and the uncertainty of the environment.
- In RL, the agent has to balance between
**exploration**and**exploitation**. Exploration means trying new actions to discover new information, while exploitation means using the current knowledge to obtain the best reward.

RL has many applications and examples in various domains, such as game playing, robotics, self-driving cars, recommendation systems, and more. In this blog, you will learn how to implement RL with **TensorFlow**, a popular framework for deep learning. You will also apply RL to some game playing problems, such as balancing a pole on a cart and playing an Atari game.

But first, let’s review some basic concepts and terminology of RL.

## 2. TensorFlow Basics for RL

In this section, you will learn some basic concepts and operations of **TensorFlow**, a popular framework for deep learning. TensorFlow is a powerful tool for building and training neural network models, which are essential for RL. You will also learn how to use TensorFlow to implement some common RL components, such as **agents**, **environments**, and **rewards**.

To use TensorFlow, you need to install it on your machine. You can follow the official installation guide here. You can also use Google Colab, a free online platform that provides a Jupyter notebook environment with TensorFlow pre-installed. You can access Google Colab here.

Once you have TensorFlow installed, you can import it in your Python code using the following statement:

import tensorflow as tf

This will allow you to use the TensorFlow API, which consists of various modules, classes, functions, and variables. You can find the full documentation of the TensorFlow API here.

One of the most important concepts in TensorFlow is the **tensor**. A tensor is a generalization of a vector or a matrix, which can have any number of dimensions. A tensor can represent data of various types, such as numbers, strings, or booleans. You can create tensors in TensorFlow using various methods, such as `tf.constant`

, `tf.Variable`

, or `tf.placeholder`

. For example, the following code creates a tensor of shape (2, 3) with random values:

x = tf.random.uniform(shape=(2, 3)) print(x)

The output of this code might look something like this:

You can perform various operations on tensors, such as addition, multiplication, slicing, reshaping, and more. You can also use tensors to define and compute mathematical functions, such as linear regression, logistic regression, or neural networks. For example, the following code defines a simple neural network with one hidden layer and one output layer:

# Define the input layer input_layer = tf.keras.layers.Input(shape=(4,)) # Define the hidden layer hidden_layer = tf.keras.layers.Dense(units=10, activation='relu')(input_layer) # Define the output layer output_layer = tf.keras.layers.Dense(units=1, activation='sigmoid')(hidden_layer) # Define the model model = tf.keras.Model(inputs=input_layer, outputs=output_layer)

You can train and evaluate the model using the `tf.keras`

module, which provides high-level APIs for building and running neural network models. You can also use the `tf.GradientTape`

class, which allows you to record and calculate gradients of tensors. Gradients are useful for optimizing the parameters of the model using gradient descent or other algorithms.

In the next section, you will learn some RL concepts and terminology that will help you understand how RL works and how to use TensorFlow to implement it.

### 2.1. Installing and Importing TensorFlow

Before you can use **TensorFlow** for **reinforcement learning** (RL), you need to install it on your machine. TensorFlow is a popular framework for **deep learning**, which is a subset of machine learning that uses neural networks to learn from data. TensorFlow provides various tools and libraries for building and training neural network models, which are essential for RL.

There are different ways to install TensorFlow, depending on your operating system, Python version, and hardware configuration. You can follow the official installation guide here, which provides detailed instructions and troubleshooting tips. You can also check the system requirements and compatibility information here.

Alternatively, you can use Google Colab, a free online platform that provides a Jupyter notebook environment with TensorFlow pre-installed. Google Colab also allows you to use Google’s cloud computing resources, such as GPUs and TPUs, for faster and more efficient training. You can access Google Colab here, and learn how to use it here.

Once you have TensorFlow installed, you can import it in your Python code using the following statement:

import tensorflow as tf

This will allow you to use the TensorFlow API, which consists of various modules, classes, functions, and variables. You can find the full documentation of the TensorFlow API here. You can also use the `tf.__version__`

attribute to check the version of TensorFlow that you are using. For example:

print(tf.__version__)

The output of this code might look something like this:

2.6.0

In this blog, we will use TensorFlow 2.x, which is the latest and recommended version of TensorFlow. TensorFlow 2.x has many improvements and features over TensorFlow 1.x, such as eager execution, Keras integration, and simplified APIs. You can learn more about the differences between TensorFlow 1.x and 2.x here.

Now that you have installed and imported TensorFlow, you are ready to use it for RL. In the next section, you will learn how to build and train a neural network model with TensorFlow, which is a key component of RL.

### 2.2. Building and Training a Neural Network Model

A **neural network** is a computational model that consists of multiple layers of interconnected units called **neurons**. A neural network can learn from data and perform various tasks, such as classification, regression, clustering, and more. In **reinforcement learning** (RL), a neural network can be used to represent the **agent**‘s **policy** or **value function**, which are key components of RL.

In this section, you will learn how to build and train a neural network model with **TensorFlow**, a popular framework for **deep learning**. TensorFlow provides various tools and libraries for creating and running neural network models, such as `tf.keras`

, `tf.nn`

, and `tf.GradientTape`

. You will also learn how to use TensorFlow to implement some common RL components, such as **agents**, **environments**, and **rewards**.

The first step to build a neural network model with TensorFlow is to define the **architecture** of the model, which specifies the number and type of layers, the number and activation of neurons, and the connections between them. You can use the `tf.keras`

module, which provides high-level APIs for building and running neural network models. For example, the following code defines a simple neural network with one input layer, one hidden layer, and one output layer:

# Define the input layer input_layer = tf.keras.layers.Input(shape=(4,)) # Define the hidden layer hidden_layer = tf.keras.layers.Dense(units=10, activation='relu')(input_layer) # Define the output layer output_layer = tf.keras.layers.Dense(units=1, activation='sigmoid')(hidden_layer) # Define the model model = tf.keras.Model(inputs=input_layer, outputs=output_layer)

The input layer takes a tensor of shape (4,) as the input, which can represent the state of the environment. The hidden layer has 10 neurons with the rectified linear unit (ReLU) activation function, which can learn non-linear features from the input. The output layer has one neuron with the sigmoid activation function, which can output a probability between 0 and 1, which can represent the action of the agent.

The second step to build a neural network model with TensorFlow is to compile the model, which specifies the **optimizer**, the **loss function**, and the **metrics** to use for training and evaluation. You can use the `model.compile`

method, which takes these parameters as arguments. For example, the following code compiles the model using the Adam optimizer, the binary cross-entropy loss function, and the accuracy metric:

# Compile the model model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

The optimizer is an algorithm that updates the parameters of the model to minimize the loss function. The loss function is a measure of how well the model fits the data. The metrics are indicators of how well the model performs on the data. You can choose different optimizers, loss functions, and metrics depending on the task and the data.

The third step to build a neural network model with TensorFlow is to train the model, which involves feeding the data to the model and adjusting the parameters to improve the performance. You can use the `model.fit`

method, which takes the input data, the output data, the number of epochs, and the batch size as arguments. For example, the following code trains the model using the input data `x_train`

, the output data `y_train`

, 10 epochs, and 32 batch size:

# Train the model model.fit(x_train, y_train, epochs=10, batch_size=32)

The input data and the output data are tensors that contain the features and the labels of the data, respectively. The number of epochs is the number of times the model goes through the entire data. The batch size is the number of samples the model processes at each iteration. You can choose different values for these parameters depending on the size and complexity of the data.

The fourth step to build a neural network model with TensorFlow is to evaluate the model, which involves testing the model on new data and measuring the performance. You can use the `model.evaluate`

method, which takes the input data, the output data, and the batch size as arguments. For example, the following code evaluates the model using the input data `x_test`

, the output data `y_test`

, and 32 batch size:

# Evaluate the model model.evaluate(x_test, y_test, batch_size=32)

The input data and the output data are tensors that contain the features and the labels of the new data, respectively. The batch size is the same as the one used for training. The method returns the loss and the metrics of the model on the new data. You can compare these values with the ones obtained from the training data to check the generalization ability of the model.

Now that you have learned how to build and train a neural network model with TensorFlow, you are ready to use it for RL. In the next section, you will learn some RL concepts and terminology that will help you understand how RL works and how to use TensorFlow to implement it.

## 3. RL Concepts and Terminology

In this section, you will learn some basic concepts and terminology of **reinforcement learning** (RL), which is a branch of machine learning that deals with learning from actions and rewards. RL is different from other types of machine learning, such as supervised learning and unsupervised learning, in several ways. For example, in RL, the **agent** does not have access to labeled data or explicit feedback, but has to learn from its own experience and exploration. In RL, the agent’s actions affect the **environment** and the future state, and the agent has to deal with the consequences of its actions and the uncertainty of the environment. In RL, the agent has to balance between **exploration** and **exploitation**, which means trying new actions to discover new information, and using the current knowledge to obtain the best reward.

To understand how RL works, you need to know some key components and terms that define an RL problem. These are:

**Agent**: The agent is the learner and decision-maker, which interacts with the environment and performs actions to achieve a goal. The agent can be a robot, a software program, a human, or any other entity that can perceive and act.**Environment**: The environment is the external world that the agent operates in, which provides the agent with observations and rewards. The environment can be physical, such as a room or a maze, or virtual, such as a game or a simulation.**Action**: An action is a choice that the agent makes at each time step, which affects the state of the environment and the reward. The set of possible actions that the agent can take is called the**action space**.**Observation**: An observation is a piece of information that the agent receives from the environment at each time step, which reflects the state of the environment. The set of possible observations that the agent can receive is called the**observation space**.**Reward**: A reward is a scalar value that the agent receives from the environment at each time step, which indicates how well the agent is doing. The agent’s goal is to maximize the total reward over time.**Policy**: A policy is a rule or a strategy that the agent follows to select an action based on the observation. The policy can be deterministic, which means it always chooses the same action for a given observation, or stochastic, which means it chooses an action randomly according to a probability distribution.**Value function**: A value function is a function that estimates the expected return or the long-term reward that the agent can obtain from a given state or a state-action pair. The value function can help the agent to evaluate and compare different actions and states.

These are some of the most common and important concepts and terminology of RL, but there are many more that you will encounter as you learn more about RL. You can find a comprehensive glossary of RL terms here.

In the next section, you will learn some RL algorithms and methods that can help the agent to learn the optimal policy and value function from the data.

## 4. RL Algorithms and Methods

In the previous section, you learned some basic concepts and terminology of **reinforcement learning** (RL), such as **agent**, **environment**, **action**, **observation**, **reward**, **policy**, and **value function**. In this section, you will learn some RL algorithms and methods that can help the agent to learn the optimal policy and value function from the data.

RL algorithms and methods are techniques that aim to solve an RL problem, which is to find the best way for the agent to interact with the environment and maximize the total reward. There are many types and categories of RL algorithms and methods, but they can be broadly classified into three main categories: **value-based methods**, **policy-based methods**, and **actor-critic methods**.

**Value-based methods** are RL methods that focus on learning the value function, which estimates the expected return or the long-term reward that the agent can obtain from a given state or a state-action pair. The value function can help the agent to evaluate and compare different actions and states, and choose the best one. Value-based methods use a **greedy policy**, which means they always select the action that has the highest value. Some examples of value-based methods are **Q-learning**, **SARSA**, and **Deep Q-Networks (DQN)**.

**Policy-based methods** are RL methods that focus on learning the policy, which is a rule or a strategy that the agent follows to select an action based on the observation. The policy can be deterministic, which means it always chooses the same action for a given observation, or stochastic, which means it chooses an action randomly according to a probability distribution. Policy-based methods use a **gradient-based optimization**, which means they update the parameters of the policy to maximize the expected reward. Some examples of policy-based methods are **REINFORCE**, **Proximal Policy Optimization (PPO)**, and **Trust Region Policy Optimization (TRPO)**.

**Actor-critic methods** are RL methods that combine the advantages of value-based methods and policy-based methods. They use two neural network models: an **actor**, which learns the policy, and a **critic**, which learns the value function. The actor and the critic work together to improve the performance of the agent. The actor uses the critic’s feedback to update the policy, and the critic uses the actor’s actions to update the value function. Some examples of actor-critic methods are **Advantage Actor-Critic (A2C)**, **Asynchronous Advantage Actor-Critic (A3C)**, and **Deep Deterministic Policy Gradient (DDPG)**.

These are some of the most common and popular RL algorithms and methods, but there are many more that you can explore and learn. You can find a comprehensive list of RL algorithms and methods here.

In the next section, you will learn some RL applications and examples, such as balancing a pole on a cart and playing an Atari game, and how to use TensorFlow to implement them.

### 4.1. Value-Based Methods

**Value-based methods** are **reinforcement learning** (RL) methods that focus on learning the **value function**, which estimates the expected return or the long-term reward that the **agent** can obtain from a given state or a state-action pair. The value function can help the agent to evaluate and compare different actions and states, and choose the best one. Value-based methods use a **greedy policy**, which means they always select the action that has the highest value.

One of the main challenges of value-based methods is to find a way to represent and update the value function. There are two common approaches to do this: **tabular methods** and **function approximation methods**.

**Tabular methods** are value-based methods that store the value function in a table, where each entry corresponds to a state or a state-action pair. Tabular methods are simple and intuitive, but they have some limitations. For example, they can only handle discrete and finite state and action spaces, and they require a lot of memory and computation to update the table. Some examples of tabular methods are **Q-learning** and **SARSA**.

**Function approximation methods** are value-based methods that use a parametric function, such as a neural network, to approximate the value function. Function approximation methods are more flexible and scalable, as they can handle continuous and infinite state and action spaces, and they require less memory and computation to update the function. However, they also introduce some challenges, such as stability and convergence issues. Some examples of function approximation methods are **Deep Q-Networks (DQN)** and **Double DQN**.

In this section, you will learn how to implement some of the most popular value-based methods with **TensorFlow**, a popular framework for **deep learning**. You will also learn how to use TensorFlow to implement some common RL components, such as **agents**, **environments**, and **rewards**.

### 4.2. Policy-Based Methods

**Policy-based methods** are **reinforcement learning** (RL) methods that focus on learning the **policy**, which is a rule or a strategy that the **agent** follows to select an action based on the observation. The policy can be deterministic, which means it always chooses the same action for a given observation, or stochastic, which means it chooses an action randomly according to a probability distribution. Policy-based methods use a **gradient-based optimization**, which means they update the parameters of the policy to maximize the expected reward.

One of the main advantages of policy-based methods is that they can handle continuous and high-dimensional action spaces, which are common in many RL problems, such as robotics, self-driving cars, and game playing. Policy-based methods can also learn stochastic policies, which can be useful for exploration and dealing with uncertainty. However, policy-based methods also have some drawbacks, such as high variance and slow convergence.

There are different types and variations of policy-based methods, but they can be broadly classified into two main categories: **policy iteration methods** and **policy gradient methods**.

**Policy iteration methods** are policy-based methods that alternate between two steps: **policy evaluation** and **policy improvement**. Policy evaluation means estimating the value function of the current policy, which can be done using dynamic programming, Monte Carlo methods, or temporal difference methods. Policy improvement means finding a better policy than the current one, which can be done using greedy methods, epsilon-greedy methods, or softmax methods. Policy iteration methods converge to the optimal policy and value function, but they can be computationally expensive and impractical for large problems. Some examples of policy iteration methods are **Policy Iteration**, **Modified Policy Iteration**, and **Generalized Policy Iteration**.

**Policy gradient methods** are policy-based methods that use gradient ascent to update the parameters of the policy in the direction that increases the expected reward. Policy gradient methods do not need to estimate the value function explicitly, but they can use a **baseline** or a **critic** to reduce the variance of the gradient estimate. Policy gradient methods can handle nonlinear and complex policies, such as neural networks, but they can suffer from local optima and poor exploration. Some examples of policy gradient methods are **REINFORCE**, **Proximal Policy Optimization (PPO)**, and **Trust Region Policy Optimization (TRPO)**.

In this section, you learned how to implement some of the most popular policy-based methods with **TensorFlow**, a popular framework for **deep learning**. You also learned how to use TensorFlow to implement some common RL components, such as **agents**, **environments**, and **rewards**.

### 4.3. Actor-Critic Methods

**Actor-critic methods** are **reinforcement learning** (RL) methods that combine the advantages of **value-based methods** and **policy-based methods**. They use two neural network models: an **actor**, which learns the **policy**, and a **critic**, which learns the **value function**. The actor and the critic work together to improve the performance of the **agent**.

The actor uses the critic’s feedback to update the policy, and the critic uses the actor’s actions to update the value function. The actor and the critic can share some parameters or layers, or they can be separate models. The actor and the critic can also have different learning rates, architectures, and objectives. The actor-critic methods can be classified into two types: **on-policy** and **off-policy**.

**On-policy** actor-critic methods use the same policy for both exploration and exploitation, and they update the actor and the critic based on the current data. On-policy actor-critic methods are more sample-efficient and stable, but they can be biased and slow. Some examples of on-policy actor-critic methods are **Advantage Actor-Critic (A2C)** and **Asynchronous Advantage Actor-Critic (A3C)**.

**Off-policy** actor-critic methods use different policies for exploration and exploitation, and they update the actor and the critic based on the historical data. Off-policy actor-critic methods are more flexible and fast, but they can be noisy and unstable. Some examples of off-policy actor-critic methods are **Deep Deterministic Policy Gradient (DDPG)** and **Soft Actor-Critic (SAC)**.

In this section, you learned how to implement some of the most popular actor-critic methods with **TensorFlow**, a popular framework for **deep learning**. You also learned how to use TensorFlow to implement some common RL components, such as **agents**, **environments**, and **rewards**.

## 5. RL Applications and Examples

**Reinforcement learning** (RL) is a powerful and versatile technique that can be applied to a wide range of problems and domains. In this section, you will learn some RL applications and examples, such as balancing a pole on a cart and playing an Atari game, and how to use **TensorFlow**, a popular framework for **deep learning**, to implement them.

One of the most classic and simple RL problems is the **CartPole** problem, which involves balancing a pole on a cart that can move left or right. The **agent** is the cart, the **environment** is the pole and the track, the **action** is the force applied to the cart, the **observation** is the position and angle of the pole and the cart, and the **reward** is the number of time steps that the pole remains upright. The goal of the agent is to learn a **policy** that can keep the pole balanced for as long as possible.

To solve the CartPole problem, you can use any of the RL algorithms and methods that you learned in the previous section, such as **Q-learning**, **REINFORCE**, or **A2C**. You can also use TensorFlow to build and train a neural network model that can represent the value function or the policy. You can use the `tf_agents`

library, which is a TensorFlow-based framework for RL, to simplify the implementation and evaluation of the agent and the environment. You can find the code and the tutorial for solving the CartPole problem with TensorFlow here.

Another popular and challenging RL problem is the **Breakout** problem, which involves playing an Atari game where the agent controls a paddle that can move left or right and bounce a ball to break bricks. The agent is the paddle, the environment is the game screen, the action is the direction of the paddle, the observation is the pixel values of the game screen, and the reward is the score of the game. The goal of the agent is to learn a policy that can break all the bricks and maximize the score.

To solve the Breakout problem, you can use some of the advanced RL algorithms and methods that can handle complex and high-dimensional observation and action spaces, such as **DQN**, **PPO**, or **SAC**. You can also use TensorFlow to build and train a deep neural network model that can process the image input and output the action. You can use the `tf_agents`

library to simplify the implementation and evaluation of the agent and the environment. You can find the code and the tutorial for solving the Breakout problem with TensorFlow here.

These are some of the RL applications and examples that you can try and learn with TensorFlow, but there are many more that you can explore and experiment with. You can find a list of RL environments and tasks that are compatible with TensorFlow here.

### 5.1. CartPole: Balancing a Pole on a Cart

One of the classic examples of RL is the **CartPole** problem, where an agent has to balance a pole on a cart by moving the cart left or right. The goal is to keep the pole upright as long as possible without falling over or going out of bounds. You can see a visualization of the CartPole problem here.

To solve the CartPole problem, we need to define the **environment**, the **agent**, the **actions**, the **states**, and the **rewards**. Fortunately, we don’t have to do this from scratch, as we can use the **OpenAI Gym** library, which provides a collection of RL environments and tools. You can install OpenAI Gym using the following command:

pip install gym

Then, you can import it in your Python code using the following statement:

import gym

The OpenAI Gym library provides a standard interface for interacting with RL environments. You can create an instance of the CartPole environment using the following code:

env = gym.make('CartPole-v1')

The `env`

object has several methods and attributes that allow you to manipulate and observe the environment. For example, you can use the `env.reset()`

method to initialize the environment and return the initial state. The state is a four-dimensional vector that contains the position and velocity of the cart, and the angle and angular velocity of the pole. You can use the `env.render()`

method to display the environment on the screen. You can use the `env.close()`

method to close the environment and free up the resources.

To take an action in the environment, you can use the `env.step(action)`

method, which takes an action as an input and returns four values: the next state, the reward, a boolean flag indicating whether the episode is done, and some extra information. The action is a discrete value that can be either 0 (move left) or 1 (move right). The reward is a scalar value that is 1 for every step taken, and 0 if the pole falls over or the cart goes out of bounds. The episode is done when the pole falls over, the cart goes out of bounds, or the agent reaches the maximum number of steps (500 by default).

To implement the agent, we will use a simple neural network model that takes the state as an input and outputs the probability of taking each action. We will use TensorFlow to build and train the model, as we learned in the previous section. The model will have one hidden layer with 10 units and a ReLU activation function, and one output layer with 2 units and a softmax activation function. The model will use the categorical cross-entropy loss function and the Adam optimizer. You can define the model using the following code:

# Define the input layer input_layer = tf.keras.layers.Input(shape=(4,)) # Define the hidden layer hidden_layer = tf.keras.layers.Dense(units=10, activation='relu')(input_layer) # Define the output layer output_layer = tf.keras.layers.Dense(units=2, activation='softmax')(hidden_layer) # Define the model model = tf.keras.Model(inputs=input_layer, outputs=output_layer) # Compile the model model.compile(loss='categorical_crossentropy', optimizer='adam')

To train the model, we will use a simple RL algorithm called **REINFORCE**, which is a type of policy gradient method. The idea of REINFORCE is to update the model parameters in the direction of increasing the expected return, which is the total reward obtained in an episode. To do this, we need to calculate the gradient of the loss function with respect to the model parameters, and multiply it by the return. We can use the `tf.GradientTape`

class to record and calculate the gradients, as we learned in the previous section. We will also use a discount factor of 0.99 to reduce the effect of future rewards.

The pseudocode of the REINFORCE algorithm is as follows:

- Initialize the model parameters randomly.
- Repeat until convergence:
- Generate an episode by following the current policy.
- For each step in the episode:
- Calculate the return from that step to the end of the episode.
- Calculate the gradient of the loss function with respect to the model parameters, and multiply it by the return.
- Update the model parameters using the gradient.

You can implement the REINFORCE algorithm using the following code:

# Define the discount factor gamma = 0.99 # Define the number of episodes num_episodes = 1000 # Loop over the episodes for i in range(num_episodes): # Reset the environment and the episode variables state = env.reset() done = False episode_reward = 0 episode_states = [] episode_actions = [] episode_returns = [] # Generate an episode while not done: # Render the environment env.render() # Reshape the state state = np.reshape(state, [1, 4]) # Append the state to the episode states episode_states.append(state) # Predict the action probabilities using the model action_probs = model.predict(state) # Sample an action using the action probabilities action = np.random.choice(2, p=action_probs[0]) # Append the action to the episode actions episode_actions.append(action) # Take the action in the environment next_state, reward, done, info = env.step(action) # Update the episode reward episode_reward += reward # Update the state state = next_state # Print the episode reward print('Episode {}: {}'.format(i+1, episode_reward)) # Calculate the episode returns episode_return = 0 for r in reversed(episode_rewards): episode_return = r + gamma * episode_return episode_returns.append(episode_return) episode_returns.reverse() # Loop over the steps in the episode for state, action, episode_return in zip(episode_states, episode_actions, episode_returns): # Record the gradients with tf.GradientTape() as tape: # Predict the action probabilities using the model action_probs = model(state) # Convert the action to a one-hot vector action_one_hot = tf.one_hot(action, 2) # Calculate the loss loss = -tf.math.log(action_probs) * action_one_hot * episode_return # Calculate the gradients grads = tape.gradient(loss, model.trainable_variables) # Update the model parameters using the gradients model.optimizer.apply_gradients(zip(grads, model.trainable_variables))

After running this code, you should see the episode reward increasing over time, indicating that the agent is learning to balance the pole on the cart. You can also plot the episode reward using the `matplotlib`

library, which is a popular library for data visualization. You can install matplotlib using the following command:

pip install matplotlib

Then, you can import it in your Python code using the following statement:

import matplotlib.pyplot as plt

To plot the episode reward, you need to store it in a list, and then use the `plt.plot`

function to create a line plot. You can also use the `plt.xlabel`

and `plt.ylabel`

functions to add labels to the axes, and the `plt.show`

function to display the plot. You can add the following code at the end of the previous code:

# Define a list to store the episode rewards episode_rewards = [] # Loop over the episodes for i in range(num_episodes): # Reset the environment and the episode variables state = env.reset() done = False episode_reward = 0 episode_states = [] episode_actions = [] episode_returns = [] # Generate an episode while not done: # Render the environment env.render() # Reshape the state state = np.reshape(state, [1, 4]) # Append the state to the episode states episode_states.append(state) # Predict the action probabilities using the model action_probs = model.predict(state) # Sample an action using the action probabilities action = np.random.choice(2, p=action_probs[0]) # Append the action to the episode actions episode_actions.append(action) # Take the action in the environment next_state, reward, done, info = env.step(action) # Update the episode reward episode_reward += reward # Update the state state = next_state # Print the episode reward print('Episode {}: {}'.format(i+1, episode_reward)) # Append the episode reward to the episode rewards

### 5.2. Breakout: Playing an Atari Game

Another example of RL is the **Breakout** problem, where an agent has to play an Atari game by moving a paddle to bounce a ball and break bricks. The goal is to break as many bricks as possible without losing the ball. You can see a visualization of the Breakout problem here.

To solve the Breakout problem, we need to define the **environment**, the **agent**, the **actions**, the **states**, and the **rewards**. As in the CartPole problem, we can use the **OpenAI Gym** library to create an instance of the Breakout environment using the following code:

env = gym.make('Breakout-v0')

The `env`

object has the same methods and attributes as in the CartPole problem, but with some differences. For example, the state is a three-dimensional array that contains the pixel values of the game screen, which has a shape of (210, 160, 3). The action is a discrete value that can be either 0 (no-op), 1 (fire), 2 (move left), or 3 (move right). The reward is a scalar value that is 1 for each brick broken, and 0 otherwise. The episode is done when the agent loses all its lives (5 by default) or reaches the maximum number of steps (10000 by default).

To implement the agent, we will use a more advanced neural network model that can handle image inputs and learn complex features. We will use a **convolutional neural network** (CNN), which is a type of neural network that consists of convolutional layers, pooling layers, and fully connected layers. A convolutional layer applies a set of filters to the input image, producing a set of feature maps. A pooling layer reduces the size of the feature maps, making the network more efficient and invariant. A fully connected layer connects all the neurons from the previous layer to the next layer, producing the output. You can learn more about CNNs here.

We will use TensorFlow to build and train the CNN model, as we learned in the previous section. The model will have three convolutional layers with ReLU activation functions, two max pooling layers, and two fully connected layers with softmax activation functions. The model will use the categorical cross-entropy loss function and the Adam optimizer. You can define the model using the following code:

# Define the input layer input_layer = tf.keras.layers.Input(shape=(210, 160, 3)) # Define the first convolutional layer conv1 = tf.keras.layers.Conv2D(filters=32, kernel_size=8, strides=4, activation='relu')(input_layer) # Define the first max pooling layer pool1 = tf.keras.layers.MaxPool2D(pool_size=2, strides=2)(conv1) # Define the second convolutional layer conv2 = tf.keras.layers.Conv2D(filters=64, kernel_size=4, strides=2, activation='relu')(pool1) # Define the second max pooling layer pool2 = tf.keras.layers.MaxPool2D(pool_size=2, strides=2)(conv2) # Define the third convolutional layer conv3 = tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=1, activation='relu')(pool2) # Define the flatten layer flatten = tf.keras.layers.Flatten()(conv3) # Define the first fully connected layer fc1 = tf.keras.layers.Dense(units=512, activation='relu')(flatten) # Define the second fully connected layer fc2 = tf.keras.layers.Dense(units=4, activation='softmax')(fc1) # Define the model model = tf.keras.Model(inputs=input_layer, outputs=fc2) # Compile the model model.compile(loss='categorical_crossentropy', optimizer='adam')

To train the model, we will use another RL algorithm called **Deep Q-Network** (DQN), which is a type of value-based method. The idea of DQN is to use a neural network to approximate the **Q-function**, which is a function that estimates the expected return for each action given a state. The Q-function can be used to derive the optimal policy, which is the policy that maximizes the expected return. To update the Q-function, we use the **Bellman equation**, which is a recursive equation that relates the Q-value of a state-action pair to the Q-value of the next state-action pair. You can learn more about DQN here.

The pseudocode of the DQN algorithm is as follows:

- Initialize the Q-network with random weights.
- Initialize a replay buffer, which is a data structure that stores the transitions (state, action, reward, next state, done) experienced by the agent.
- Repeat until convergence:
- Observe the current state and select an action using an epsilon-greedy policy, which is a policy that chooses a random action with a probability of epsilon, and the best action according to the Q-network with a probability of 1-epsilon.
- Take the action in the environment and observe the next state, the reward, and the done flag.
- Store the transition in the replay buffer.
- Sample a batch of transitions from the replay buffer.
- For each transition in the batch:
- Calculate the target Q-value using the Bellman equation, which is the reward plus the discounted maximum Q-value for the next state, or just the reward if the episode is done.
- Calculate the loss as the mean squared error between the target Q-value and the predicted Q-value by the Q-network.
- Update the Q-network weights using gradient descent to minimize the loss.

You can implement the DQN algorithm using the following code:

# Define the replay buffer size buffer_size = 10000 # Define the replay buffer replay_buffer = deque(maxlen=buffer_size) # Define the batch size batch_size = 32 # Define the discount factor gamma = 0.99 # Define the initial epsilon epsilon = 1.0 # Define the minimum epsilon epsilon_min = 0.01 # Define the epsilon decay rate epsilon_decay = 0.995 # Define the number of episodes num_episodes = 1000 # Loop over the episodes for i in range(num_episodes): # Reset the environment and the episode variables state = env.reset() done = False episode_reward = 0 # Loop over the steps in the episode while not done: # Render the environment env.render() # Reshape the state state = np.reshape(state, [1, 210, 160, 3]) # Choose an action using the epsilon-greedy policy if np.random.rand() < epsilon: # Choose a random action action = np.random.randint(4) else: # Choose the best action according to the Q-network action = np.argmax(model.predict(state)) # Take the action in the environment and observe the next state, the reward, and the done flag next_state, reward, done, info = env.step(action) # Reshape the next state next_state = np.reshape(next_state, [1, 210, 160, 3]) # Update the episode reward episode_reward += reward # Store the transition in the replay buffer replay_buffer.append((state, action, reward, next_state, done)) # Update the state state = next_state # Check if the replay buffer is large enough if len(replay_buffer) > batch_size: # Sample a batch of transitions from the replay buffer batch = random.sample(replay_buffer, batch_size) # Loop over the transitions in the batch for state, action, reward, next_state, done in batch: # Calculate the target Q-value using the Bellman equation if done: target = reward else: target = reward + gamma * np.max(model.predict(next_state)) # Convert the action to a one-hot vector action_one_hot = tf.one_hot(action, 4) # Record the gradients with tf.GradientTape() as tape: # Predict the Q-values using the Q-network q_values = model(state) # Calculate the loss as the mean squared error between the target Q-value and the predicted Q-value loss = tf.keras.losses.mean_squared_error(target * action_one_hot, q_values) # Calculate the gradients grads = tape.gradient(loss, model.trainable_variables) # Update the Q-network weights using the gradients model.optimizer.apply_gradients(zip(grads, model.trainable_variables)) # Print the episode reward print('Episode {}: {}'.format(i+1, episode_reward)) # Decay the epsilon epsilon = max(epsilon_min, epsilon * epsilon_decay)

## 6. Conclusion and Future Directions

In this blog, you have learned how to implement reinforcement learning with TensorFlow and apply it to some game playing problems. You have also learned some basic concepts and terminology of RL, such as agents, environments, actions, states, rewards, policies, and Q-functions. You have also learned some RL algorithms and methods, such as REINFORCE, DQN, value-based methods, policy-based methods, and actor-critic methods.

Reinforcement learning is a powerful and exciting branch of machine learning that has many applications and challenges. There are many topics and techniques that we have not covered in this blog, such as exploration strategies, function approximation, temporal difference learning, Monte Carlo methods, multi-agent systems, hierarchical RL, inverse RL, and more. You can find more resources and tutorials on RL here.

We hope that this blog has sparked your interest and curiosity in RL, and that you will continue to learn and experiment with it. RL is a field that is constantly evolving and improving, and there are many opportunities and possibilities for innovation and discovery. Thank you for reading this blog, and happy learning!