Reinforcement Learning: Using Q-Learning to Drive a Taxi!

After more than 2 months without publish, I returned! Now, I wanna divide with you my last experiences studying Reinforcement Learning and solving some problems.

The first algorithm for any any newbie in Reinforcement Learning usually is Q-Learning, and why? Because it’s a very simple algorithm, easy to understand and powerful for a many problems!

In this post, we’ll build together a agent to play the Taxi-V3 game from OpenAI Gym just using numpy and a few lines of code. After this article, you’ll be able to apply Q-Learning to solve other problems in different environments.

But first, we need to understand what is Reinforcement Learning?

A Short Resume of Reinforcement Learning

The image above resume the core idea of Reinforcement Learning where we have:

• Agent: Think of the agent as our model, he is responsible for making the magic happen, like playing Pacman like a professional.
• Environment: The Environment is where the magic happens, in this example will be the Taxi-V3 game.
• Reward: Is the feedback given by the Environment to say if the action taken from agent was good or bad. The reward can be positive or negative.
• **Action:**The action taken by the Agent.
• State: Is the current situation of the Agent in Environment such: low life, without ammunition or facing a wall.

The main goal of the Agent is take actions that will maximize your future reward. So the flow is:

• Take an action;
• Receive a feedback from environment;
• Receive the new state;
• Take a new Action;

Our Agent have 2 ways to take a decision in determined situation: Exploration and Exploitation. In the Exploration, our Agent will take random decisions, this is useful to learn about the environment. In the Exploitation, our Agent will take actions based on what he already knows.

In the amazing video below, you can visualize the Reinforcement Learning in practice where we have 4 agents playing hide and seek. Don’t forget to check this!

Now, you already know what is Reinforcement Learning and why it’s so amazing field from the Artificial Intelligence!

Let’s see how Q-Learning works.

Q-Learning Resume

Like I said before, Q-Learning is a very simple to understand algorithm and very recommended to beginners in Reinforcement Learning, because it’s powerful and can be apply in a few lines of code.

Basically in Q-Learning, our we create a table with actions and states, called Q-Table. This table will help our agent to take the best action for the moment. The table looks like this:

Q-Table

But in the beginning, we start this table with 0 in all values. The idea is leave the agent explore the environment taking random actions and after, use the rewards received from these actions to populate the table, this is the Exploration.

After that, we start the Exploitation, where the agent use the table to take actions who will maximize him future reward. But in the Exploitation, the Q-Table still changing with the states, a good action in some state don’t necessary will be a good action in other state.

To decide the action to maximize the future reward, we use the formula below

After that, our agent will receive a reward from the environment, that can be negative or positive. And we’ll use the formula below to update our Q-Table:

This is how the Q-Learning Algorithm works, remember that flow:

#reinforcement-learning #agents #q-learning #artificial-intelligence #ai #deep learning

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Reinforcement Learning: Using Q-Learning to Drive a Taxi!

After more than 2 months without publish, I returned! Now, I wanna divide with you my last experiences studying Reinforcement Learning and solving some problems.

The first algorithm for any any newbie in Reinforcement Learning usually is Q-Learning, and why? Because it’s a very simple algorithm, easy to understand and powerful for a many problems!

In this post, we’ll build together a agent to play the Taxi-V3 game from OpenAI Gym just using numpy and a few lines of code. After this article, you’ll be able to apply Q-Learning to solve other problems in different environments.

But first, we need to understand what is Reinforcement Learning?

A Short Resume of Reinforcement Learning

The image above resume the core idea of Reinforcement Learning where we have:

• Agent: Think of the agent as our model, he is responsible for making the magic happen, like playing Pacman like a professional.
• Environment: The Environment is where the magic happens, in this example will be the Taxi-V3 game.
• Reward: Is the feedback given by the Environment to say if the action taken from agent was good or bad. The reward can be positive or negative.
• **Action:**The action taken by the Agent.
• State: Is the current situation of the Agent in Environment such: low life, without ammunition or facing a wall.

The main goal of the Agent is take actions that will maximize your future reward. So the flow is:

• Take an action;
• Receive a feedback from environment;
• Receive the new state;
• Take a new Action;

Our Agent have 2 ways to take a decision in determined situation: Exploration and Exploitation. In the Exploration, our Agent will take random decisions, this is useful to learn about the environment. In the Exploitation, our Agent will take actions based on what he already knows.

In the amazing video below, you can visualize the Reinforcement Learning in practice where we have 4 agents playing hide and seek. Don’t forget to check this!

Now, you already know what is Reinforcement Learning and why it’s so amazing field from the Artificial Intelligence!

Let’s see how Q-Learning works.

Q-Learning Resume

Like I said before, Q-Learning is a very simple to understand algorithm and very recommended to beginners in Reinforcement Learning, because it’s powerful and can be apply in a few lines of code.

Basically in Q-Learning, our we create a table with actions and states, called Q-Table. This table will help our agent to take the best action for the moment. The table looks like this:

Q-Table

But in the beginning, we start this table with 0 in all values. The idea is leave the agent explore the environment taking random actions and after, use the rewards received from these actions to populate the table, this is the Exploration.

After that, we start the Exploitation, where the agent use the table to take actions who will maximize him future reward. But in the Exploitation, the Q-Table still changing with the states, a good action in some state don’t necessary will be a good action in other state.

To decide the action to maximize the future reward, we use the formula below

After that, our agent will receive a reward from the environment, that can be negative or positive. And we’ll use the formula below to update our Q-Table:

This is how the Q-Learning Algorithm works, remember that flow:

#reinforcement-learning #agents #q-learning #artificial-intelligence #ai #deep learning

Deep Reinforcement Learning for Video Games Made Easy

In this post, we will investigate how easily we can train a Deep Q-Network (DQN) agent (Mnih et al., 2015) for Atari 2600 games using the Google reinforcement learning library Dopamine. While many RL libraries exist, this library is specifically designed with four essential features in mind:

• Easy experimentation
• Flexible development
• Compact and reliable
• Reproducible

_We believe these principles makes __Dopamine _one of the best RL learning environment available today. Additionally, we even got the library to work on Windows, which we think is quite a feat!

In my view, the visualization of any trained RL agent is an absolute must in reinforcement learning! Therefore, we will (of course) include this for our own trained agent at the very end!

We will go through all the pieces of code required (which is** minimal compared to other libraries**), but you can also find all scripts needed in the following Github repo.

1. Brief Introduction to Reinforcement Learning and Deep Q-Learning

The general premise of deep reinforcement learning is to

“derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations.”

• Mnih et al. (2015)

As stated earlier, we will implement the DQN model by Deepmind, which only uses raw pixels and game score as input. The raw pixels are processed using convolutional neural networks similar to image classification. The primary difference lies in the objective function, which for the DQN agent is called the optimal action-value function

where_ rₜ is the maximum sum of rewards at time t discounted by γ, obtained using a behavior policy π = P(a_∣_s)_ for each observation-action pair.

There are relatively many details to Deep Q-Learning, such as Experience Replay (Lin, 1993) and an _iterative update rule. _Thus, we refer the reader to the original paper for an excellent walk-through of the mathematical details.

One key benefit of DQN compared to previous approaches at the time (2015) was the ability to outperform existing methods for Atari 2600 games using the same set of hyperparameters and only pixel values and game score as input, clearly a tremendous achievement.

2. Installation

This post does not include instructions for installing Tensorflow, but we do want to stress that you can use both the CPU and GPU versions.

Nevertheless, assuming you are using Python 3.7.x, these are the libraries you need to install (which can all be installed via pip):

tensorflow-gpu=1.15   (or tensorflow==1.15  for CPU version)
cmake
dopamine-rl
atari-py
matplotlib
pygame
seaborn
pandas

#reinforcement-learning #q-learning #games #machine-learning #deep-learning #deep learning

Attend The Full Day Hands-On Workshop On Reinforcement Learning

The Association of Data Scientists (AdaSci), a global professional body of data science and ML practitioners, is holding a full-day workshop on building games using reinforcement learning on Saturday, February 20.

Artificial intelligence systems are outperforming humans at many tasks, starting from driving cars, recognising images and objects, generating voices to imitating art, predicting weather, playing chess etc. AlphaGo, DOTA2, StarCraft II etc are a study in reinforcement learning.

Reinforcement learning enables the agent to learn and perform a task under uncertainty in a complex environment. The machine learning paradigm is currently applied to various fields like robotics, pattern recognition, personalised medical treatment, drug discovery, speech recognition, and more.

With an increase in the exciting applications of reinforcement learning across the industries, the demand for RL experts has soared. Taking the cue, the Association of Data Scientists, in collaboration with Analytics India Magazine, is bringing an extensive workshop on reinforcement learning aimed at developers and machine learning practitioners.

#ai workshops #deep reinforcement learning workshop #future of deep reinforcement learning #reinforcement learning #workshop on a saturday #workshop on deep reinforcement learning

Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

• Temporal difference learning (TD learning)
• Parameters
• QL & SARSA
• Comparison
• Implementation
• Conclusion

We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning

Reinforcement Learning : Q-Learning

Q -learning, as the name suggests, it’s a learning-based algorithm in reinforcement learning. Q-Learning is a basic form of Reinforcement Learning algorithm that uses Q-values (also called action values) to iteratively improve the behavior of the learning agent.

The main objective of Q-learning is to find the best optimal policy, discussed previously that maximizes the cumulative rewards (sum of all rewards). So, in other words, the goal of Q-learning is to find the optimal policy by learning the optimal Q-values for each state-action pair.

Q-Learning simplified with an example

Let’s consider a ROBOT who starts form the starting position (S) and its goal is to move the endpoint (G). This is a game: Frozen Lake where; S=starting point, F=frozen surface, H=hole, and G=goal. The robot can either move _f_orward, _d_ownward, _l_eft, _r_ight.

Robots win if reaches Goal and looses if falls in a Hole.

Thanks for gif:

Now, the obvious question is: How do we train a robot to reach the end goal with the shortest path without stepping on a hole?

Introduction to Q-TABLE

Q-Table is a simple lookup table where we calculate the maximum expected future rewards for future action at each state. Q-table is created as per the possible action the robot can perform. Q-table is initialized with null values.

Initial Q-table

Each Q-table score will be the maximum expected future reward that the robot will get if it takes that action at that state. This is an iterative process, as we need to improve the Q-Table at each iteration.

But the questions are:

• How do we calculate the values of the Q-table**?**
• Are the values available or predefined**?**

To learn each value of the Q-table, we use the** Q-Learning algorithm. **As we discussed in the earlier part, we use the Bellman Equation to find optimal values for the action-value pair.

As we start to explore the environment**,** the Q-function gives us better and better approximations by continuously updating the Q-values in the table.

#machine-learning #deep-learning #q-learning #deep learning