In this tutorial, we’ll learn more about continuous Reinforcement Learning agents and how to teach BipedalWalker-v3 to walk! First of all, I should mention that this tutorial continues my previous tutorial, where I covered PPO with discrete actions.

To develop a continuous action space Proximal Policy Optimization algorithm, we must first understand their difference. Because **LunarLander-v2** environment also has a continuous environment called **LunarLanderContinuous-v2**, I’ll mention what the difference between them is:

**LunarLander-v2**has a Discrete(4) action space. This means there are four outputs (left engine, right engine, main engine, and do nothing), and we send to the environment…

Welcome to another part of my step-by-step reinforcement learning tutorial with gym and TensorFlow 2. I’ll show you how to implement a Reinforcement Learning algorithm known as Proximal Policy Optimization (PPO) for teaching an AI agent how to land a rocket (Lunarlander-v2). By the end of this tutorial, you’ll get an idea of how to apply an on-policy learning method in an actor-critic framework to learn navigating any discrete game environment. Next, followed by this tutorial I will create a similar tutorial with a continuous environment. …

In 2018 OpenAI made a breakthrough in Deep Reinforcement Learning, this was possible only because of solid hardware architecture and using the state of the art’s algorithm: Proximal Policy Optimization.

The main idea of Proximal Policy Optimization is to avoid having too large a policy update. To do that, we use a ratio that tells us the difference between our new and old policy and clip this ratio from 0.8 to 1.2. Doing that will ensure that the policy update will not be too large.

This tutorial will dive into understanding the PPO architecture and implement a Proximal Policy…

In this tutorial, I will implement the Asynchronous Advantage Actor-Critic (A3C) algorithm in Tensorflow and Keras. We will use it to solve a simple challenge in the Pong environment! If you are new to Deep Learning and Reinforcement Learning, I suggest checking out my previous tutorials before going through this post to understand all the building blocks that will be utilized here. If you have been following the series: thank you! Writing these tutorials, I have learned so much about RL in the past months and am happy to share it with everyone.

So what is A3C? Google’s DeepMind…

Since the beginning of this Reinforcement Learning tutorial series, I’ve covered two different reinforcement learning methods: Value-based methods (Q-learning, Deep Q-learning…) and Policy-based methods (REINFORCE with Policy Gradients).

Both of these methods have considerable drawbacks. That’s why, today, I’ll try another type of Reinforcement Learning method, which we can call a ‘hybrid method’: Actor-Critic. The actor-Critic algorithm is a Reinforcement Learning agent that combines value optimization and policy optimization approaches. More specifically, the Actor-Critic combines the Q-learning and Policy Gradient algorithms. The resulting algorithm obtained at the high level involves a cycle that shares features between:

- Actor: a PG…

Up to this moment, we covered the most popular tutorials related to DQN — value-based reinforcement learning algorithms. In deep reinforcement learning, deep Q-learning Networks are relatively simple. DQN’s are comparatively simple to their credit, but many other deep RL agents also make efficient use of the training samples available. That said, DQN agents do have drawbacks. Most notable are:

- Suppose the possible number of state-action pairs is relatively large in a given environment. In that case, the Q-function can become highly complicated, so it becomes intractable to estimate the optimal Q-value.
- Even in situations where finding Q is…

There’s a massive difference between reading about Reinforcement Learning and implementing it. In this tutorial, I’ll implement a Deep Neural Network for Reinforcement Learning (Deep Q Network), and we will see it learns and finally becomes good enough to beat the computer in Pong!

By the end of this post, you’ll be able to do the following:

- Write a Neural Network from scratch;
- Implement a Deep Q Network with Reinforcement Learning;
- Build an A.I. for Pong that can beat the computer in less than 300 lines of Python;
- Use OpenAI gym.

Considering the limited time and for learning purposes…

This tutorial will show you how to implement one of the most groundbreaking Reinforcement Learning algorithms — DQN with pixels. After the end of this tutorial, you will create an agent that successfully plays almost ‘any’ game using only pixel inputs.

We’ve used game-specific inputs in all my previous DQN tutorials (like cart position or pole angle). Now we will be more general and use something that all games have in common — pixels. To begin with, I would like to come back to our first DQN tutorial, where we wrote our first agent code to take actions randomly…

So, in our previous tutorial, we implemented the Double Dueling DQN Network model, and we saw that our agent improved this way slightly. Now it’s time to implement Prioritized Experience Replay (PER), introduced in 2015 by Tom Schaul. The paper idea is that some experiences may be more critical than others for our training but might occur less frequently.

Because we sample the batch uniformly (selecting the experiences randomly), these rich experiences that occur rarely have practically no chance of being selected.

That’s why, with PER, we will try to change the sampling distribution by using a criterion to…

In the previous tutorial, I said that we’d try to implement the Prioritized Experience Replay (PER) method in the next tutorial. Still, before doing that, I decided that we should cover the Epsilon Greedy fix/prepare the source code for PER method. So this will be quite a short tutorial.

The epsilon-greedy algorithm is straightforward and occurs in several areas of machine learning. One everyday use of epsilon-greedy is in the so-called multi-armed bandit problem.

Let’s take an example. Suppose we are standing in front of three slot machines. Each of these machines payout according to a different probability distribution…