Deep Reinforcement Learning made-easy

Name: Deep Reinforcement Learning made-easy
Rating: 4.5 (28 reviews)

Reinforcement Learning for beginners to advanced learners

Highest Rated

Created byDr. Muhammad Farhan

Last updated 8/2025

English

English [Auto],

What you'll learn

To understand deep learning and reinforcement learning paradigms
To understand Architectures and optimization methods for deep neural network training
To implement deep learning methods within Tensor Flow and apply them to data
To understand the theoretical foundations and algorithms of reinforcement learning
To apply reinforcement learning algorithms to environments with complex dynamics

Course content

15 sections • 128 lectures • 18h 58m total length

Introduction to Deep Reinforcement Learning8:28
This lecture introduces reinforcement learning, covering fundamental concepts and their connection to deep learning and neural networks. The course is structured into two main parts: foundational deep learning principles, including numerical methods and coding exercises, followed by reinforcement learning, exploring different agent types and mechanisms.
Initially, deep learning is introduced to understand the neural networks that reinforcement learning (RL) agents use. Following this, the focus shifts to RL, covering agents, environments, rewards, and punishments. The course aims to make students proficient in understanding RL agents, their neural network models, and how these elements integrate to form effective learning systems.
Key topics include defining RL mechanics and components and comparing RL with supervised and unsupervised learning. A historical overview is given, from RL's origins in robotics to its applications in fields like ChatGPT's model development, where RL plays a crucial role. Practical applications and examples demonstrate how RL agents operate in dynamic scenarios, learning through trial and error to maximize cumulative rewards. The distinction between RL’s dynamic learning versus the static nature of traditional machine learning is highlighted, emphasizing RL's adaptability and real-time learning.
The lecture explains RL's core mechanisms: trial and error, delayed rewards, and sequential decision-making. These are linked to human learning patterns, where agents maximize rewards through situational actions. Comparisons with static machine learning illustrate RL's continuous adaptation in various scenarios, reinforcing its role as an evolving subset within the broader AI domain alongside machine and deep learning.
Reinforcement Learning and its main components (agent, environment, rewards)20:25
Deep reinforcement learning overview: an agent interacts with an environment through states, actions, and rewards, aiming to maximize cumulative reward while balancing exploration and exploitation to learn optimal policies.
Comparison with supervised and unsupervised learning18:21
Overview of the RL history4:06
Recent advances in Deep Reinforcement Learning5:07
Explore how recent deep reinforcement learning advances span algorithms, theory, and real-world applications in robotics, autonomous vehicles, healthcare, finance, and recommendations, with emphasis on safety and ethics.
Learning objectives for the course and Introduction to Python11:06
Explore the integration of deep learning and reinforcement learning, from artificial neural networks to CNNs, RNNs, and LSTMs, and apply deep reinforcement learning with Python and TensorFlow to complex environments.
Experts' review on Introduction of the course10:39

Deep Neural Network22:52
Deep learning frameworks2:47
Explore major deep learning frameworks, focusing on TensorFlow and PyTorch, with open source tools from Google Brain and Facebook AI. Understand TensorFlow version differences, compatibility mode, and Keras usage.
Introduction to TensorFlow and Keras13:27
Key terms in TensorFlow22:23
Explore tensor concepts in TensorFlow, from 1d to 5d tensors and color channels to constant and trainable variables, and outline model building with gradient descent.
KERAS6:47
The concept of gradient descent15:45
Explore how gradient descent optimizes neural networks by updating random weights toward the negative gradient to minimize error, including batch, stochastic, and mini-batch approaches.
Learning rate20:44
Experts' review on Gradient Descent and Neural Networks15:08
Explore how gradient descent powers neural networks to learn from data, navigate the error surface, and overcome vanishing or exploding gradients with learning-rate strategies and momentum.

Hyper parameters in Machine Learning19:51
Explore how to avoid overfitting and underfitting by managing model complexity, tuning hyperparameters, and improving the training data through augmentation, synthetic data, and preprocessing.
L1 and L2 Regularization in Regression2:54
Examine L1 and L2 regularization in regression and neural networks, comparing ridge and lasso methods. See how L1 drives weights to zero while L2 shrinks them to prevent overfitting.
Regularization in Neural networks2:10
Apply regularization to prevent overfitting by using a large network and constraining weights to stay low, rather than dropping hidden nodes, highlighting hidden layers and nodes as key hyperparameters.
Regularization in Regression2:10
Regularization prevents overfitting by constraining weights in a large deep neural network, keeping hidden layers and nodes but with reduced weights to improve model fit.
Data standardization in L1 and L2 regularization2:29
Standardize data before applying L1 and L2 regularization to ensure the lambda parameter effectively penalizes weights by scaling inputs to a common range 0-1.
Dropout Regularization6:12
Dropout regularization randomly drops a fraction of hidden layer nodes during training to prevent overfitting and encourage robust, ensemble-like predictions across networks.
Early stopping method for neural networks3:10
Saving the Model3:40
Experts' review on Saving Keras Models in Python11:56

Loss Functions33:11
Explore loss functions used in classification and regression, including binary cross entropy, hinge loss, mean squared error, mean absolute error, huber, and log cosh, and their role in optimization.
Activation Functions0:32
Activation Function: Sigmoid3:19
Activation Function: Tanh3:41
Activation Function: ReLU4:06
Activation Function: SoftMax4:08
Use softmax for multi-class outputs to produce a probability distribution; for binary outcomes, use sigmoid, and implement softmax in Keras as a dense layer activation or standalone.
Optimizers: SGD, Mini-batch descent4:59
Explore stochastic gradient descent and mini-batch gradient descent, learning how gradient estimates from small data batches update weights efficiently, with batch size as a key hyperparameter.
Experts' review on Stochastic Gradient Descent and Mini-Batch Optimization11:21

Cross-sectional data vs sequential data4:41
Compare cross-sectional data and sequential data, explain time series concepts, and show how recurrent neural networks, CNNs, and sequential ANNs handle ordered data through a word prediction case study.
Models for sequential data: ANN, CNN and Sequential ANN18:44
Explore how artificial neural networks handle sequential data, comparing ANN, CNN, and sequential ANN approaches for predicting next words with one-hot encoding and serially connected networks.
Case study of word prediction1:37
Introduction to RNN16:15
Python Code: Model Training of CNN and RNN33:49
Experts' review on Deep Learning with TensorFlow and Keras19:43
Explore TensorFlow and CNNs for image recognition, including sign language, and glimpse RNNs and LSTMs with memory gates for translation and music generation.

Review of Reinforcement Learning22:49
Introduction to Value Function Approximation1:26
Learn value function approximation to handle large or continuous state spaces in reinforcement learning and MDPs, using compact representations to generalize from observed to unseen states, including epsilon-greedy approaches.
Python Code: Value Function Approximation using CartPole12:37
Linear function approximation0:51
Explore linear function approximation in reinforcement learning and Markov decision processes, using v(s) = theta^T phi(s) to estimate values for large state spaces.
Python Code: Linear Function Approximation using CartPole1:44
Non-linear function approximation with deep neural networks0:37
Python Code: Non-Linear Function Approximation with Neural Networks3:28
Applications and limitations of Value Function Approximation0:50
Definition of Markov Decision Processes (MDPs)0:59
Explore MDPs, a powerful framework for decision making that handles uncertainty, rewards, and actions to guide reinforcement learning and related domains.
Python Code: MDPs and Bellman Equations and Value Functions9:15
Implement a grid world MDP in numpy, defining state and action spaces, a four-action policy, and Bellman value function updates with a gamma of 0.9 and a custom reward scheme.
Key components of an MDP3:45
Bellman Equations and Value Functions0:45
Explore how Bellman equations link the value of a state or action to its successor states, forming the foundation of value functions in Markov decision processes and reinforcement learning.
Policy iteration and value iteration algorithms18:34
Python Code: Policy iteration and value iteration algorithms10:41
Experts' review on Markov Decision Processes and Applications17:44
Explore reinforcement learning and Markov decision processes, covering states, actions, rewards, the reward hypothesis, and foundations like Bellman equation, value and policy iteration, plus deep and multi-agent RL.

Python Code: Introduction to Python Gym Library Documentation8:01
Review of Bellman Equations4:14
Explore Bellman equations and value functions, including state value and action value calculations, with intuitive interpretations, and review Markov reward and decision processes, and optimization of Bellman equations in MDPs.
Definition of value functions (state value, action value)2:20
Calculation of value functions using Bellman Equations1:58
Intuitive interpretation of value functions3:00
Markov Processes12:01
Markov Reward Processes14:11
Markov Decision Processes17:39
Extensions to MDPs6:56
Explore extensions to mdps, including infinite and continuous state and action spaces, continuous time with Hamiltonian Jacobi Bellman equations, and partially observable mdps with belief trees.
Experts' review on Bellman Equations and Value Functions18:14

Definition of Q-Learning1:11
Calculation of Q-Values using Q-Learning3:43
Python Code: Q-Learning and Python Gym library7:06
Comparison of Q-Learning with policy iteration and value iteration algorithms0:49
Advantages and disadvantages of Q-Learning2:13
Overview of Deep Q-Network (DQN) algorithm5:01
Architecture of a DQN model1:29
Implementation of DQN in TensorFlow3:24
Python Code: Implementation of DQN20:36
Applications and limitations of DQN3:22
Experts' review on Q-Learning in Frozen Lake25:29

Requirements

Basic python programming but not necessary

Description

This course is the integration of deep learning and reinforcement learning. The course will introduce student with deep neural networks (DNN) starting from simple neural networks (NN) to recurrent neural network and long-term short-term memory networks. NN and DNN are the part of reinforcement learning (RL) agent so the students will be explained how to design custom RL environments and use them with RL agents. After the completion of the course the students will be able:

To understand deep learning and reinforcement learning paradigms
To understand Architectures and optimization methods for deep neural network training
To implement deep learning methods within Tensor Flow and apply them to data.
To understand the theoretical foundations and algorithms of reinforcement learning.
To apply reinforcement learning algorithms to environments with complex dynamics.

Course Contents:

Introduction to Deep Reinforcement Learning
Artificial Neural Network (ANN)
ANN to Deep Neural Network (DNN)
Deep Learning Hyperparameters: Regularization
Deep Learning Hyperparameters: Activation Functions and Optimizations
Convolutional Neural Network (CNN)
CNN Architecture
Recurrent Neural Network (RNN)
RNN for Long Sequences
LSTM Network
Overview of Markov Decision Processes
Bellman Equations and Value Functions
Deep Reinforcement Learning with Q-Learning
Model-Free Prediction
Deep Reinforcement Learning with Policy Gradients
Exploration and Exploitation in Reinforcement Learning

Who this course is for:

Data Scientists
Machine Learning Engineers
Robotics Programmer

Deep Reinforcement Learning made-easy

What you'll learn

Explore related topics

Course content

Introduction7 lectures • 1hr 18min

Artificial Neural Network (ANN)4 lectures • 47min

ANN to Deep Neural Network (DNN)8 lectures • 2hr

Deep Learning Hyperparameters Regularization9 lectures • 55min

Deep Learning Hyper parameters, Activation Functions and Optimizations8 lectures • 1hr 5min

Convolutional Neural Network (CNN)4 lectures • 42min

Recurrent Neural Network (RNN)6 lectures • 1hr 35min

Reinforcement Learning: Overview of Markov Decision Processes15 lectures • 1hr 46min

Bellman Equations and Value Functions10 lectures • 1hr 29min

Deep Reinforcement Learning with Q-Learning11 lectures • 1hr 14min

Requirements

Description

Who this course is for: