This is an introduction tutorial to Reinforcement Learning. To understand everything from basics I will start with a simple game called - CartPole

This is the second reinforcement tutorial part, where we'll make our environment use two (Double) neural networks to train our main model

In this post, we’ll be covering Dueling DQN networks for reinforcement learning. This architecture is an improvement from our previous DDQN tutorial.

In this part, we'll cover the Epsilon Greedy method used in Deep Q Learning and we'll fix/prepare our source code for PER method

Now we will try to change the sampling distribution by using a criterion to define the priority of each tuple of experience.

Now I will show you how to implement DQN with CNN. After this tutorial, you'll be able to create an agent that successfully plays almost ‘any’ game using only pixel inputs

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!

To wrap up deep reinforcement learning, I’ll introduce the types of agents beyond DQN’s (Value, Model, Policy optimization, and Imitation Learning). We'll implement Policy Gradient!

Today, we'll study a Reinforcement Learning method that we can call a 'hybrid method': Actor-Critic. This algorithm combines the value optimization and policy optimization approaches

In this tutorial, I will provide an implementation of the Asynchronous Advantage Actor-Critic (A3C) algorithm in Tensorflow and Keras. We will use it to solve a simple challenge in the Pong environmens

In this tutorial, we'll dive into the understanding of the PPO architecture and we'll implement a Proximal Policy Optimization (PPO) agent that learns to play Pong-v0

In this step-by-step reinforcement learning tutorial with gym and TensorFlow 2. I’ll show you how to implement a PPO for teaching an AI agent how to land a rocket (Lunarlander-v2)

In this tutorial, we'll solve the BipedalWalker-v3 environment, which is a very hard environment in the Gym. Our agent should run very fast, should not trip himself off, should use as little energy as possible...