Q-Learning and Deep Q Networks

Course Topics

• What is Reinforcement Learning?

  # What is Reinforcement Learning? Reinforcement Learning (RL) is a subset of machine learning where an agent learns to make decisions by taking actions in an environment to maximize a cumulative rewa...

• Target Network Method

  # Target Network Method The Target Network Method is a significant enhancement in the field of Deep Q-Learning, particularly in the context of Deep Q-Networks (DQN). This method addresses the instabi...

• Quiz: Challenges in Q-Learning

  # Challenges in Q-Learning Q-Learning is a powerful reinforcement learning algorithm that has been widely used for various applications. However, it is not without its challenges. In this section, we...

• Quiz: Fundamentals of DQN

  # Quiz: Fundamentals of Deep Q Networks (DQN) ## Overview Deep Q Networks (DQN) represent a significant advancement in the field of reinforcement learning, combining Q-learning with deep learning. In...

• Exploration vs. Exploitation

  # Exploration vs. Exploitation In the field of Reinforcement Learning (RL), one of the fundamental dilemmas faced by agents is the trade-off between exploration and exploitation. This concept is cruc...

• Hyperparameter Tuning for DQN

  # Hyperparameter Tuning for DQN Hyperparameter tuning is a crucial step in optimizing Deep Q-Networks (DQN). It involves selecting the best set of hyperparameters to improve the performance of the DQ...

• Q-Learning in Continuous Spaces

  # Q-Learning in Continuous Spaces Q-Learning is a popular reinforcement learning algorithm typically used in discrete action and state spaces. However, many real-world scenarios require agents to ope...

• Quiz: Advanced Q-Learning Techniques

  # Advanced Q-Learning Techniques ## Introduction to Advanced Q-Learning Q-Learning is a powerful algorithm in reinforcement learning that allows an agent to learn how to optimally interact with an en...

• Scalability Issues in DQN

  # Scalability Issues in DQN As Deep Q-Networks (DQN) have gained traction in the field of reinforcement learning, particularly for solving complex tasks in environments with discrete action spaces, s...

• Combining Q-Learning with Neural Networks

  # Combining Q-Learning with Neural Networks In the field of reinforcement learning, **Q-Learning** is a popular model-free algorithm that aims to learn the value of actions taken in states of an envi...

• Ethics and Responsible AI in RL

  # Ethics and Responsible AI in Reinforcement Learning Reinforcement Learning (RL) is a powerful paradigm in artificial intelligence, enabling agents to learn optimal behaviors through interactions wi...

• Prioritized Experience Replay in DQN

  # Prioritized Experience Replay in DQN ## Introduction In traditional Deep Q-Learning (DQN), the experience replay mechanism allows agents to sample past experiences to break the correlation between ...

• Evaluating DQN Performance Metrics

  # Evaluating DQN Performance Metrics In this section, we will delve into the various performance metrics used to evaluate Deep Q-Networks (DQN) in real-world applications. Understanding these metrics...

• Types of Reinforcement Learning: Value-Based vs. Policy-Based

  # Types of Reinforcement Learning: Value-Based vs. Policy-Based Reinforcement Learning (RL) is a subset of machine learning where an agent learns to make decisions by interacting with an environment....

• Limitations of Traditional Q-Learning

  # Limitations of Traditional Q-Learning Q-Learning is a popular model-free reinforcement learning algorithm that enables an agent to learn how to optimally navigate an environment. Despite its effect...

• Experience Replay

  # Experience Replay Experience replay is a crucial technique in reinforcement learning, particularly in the context of Q-Learning and Deep Q Networks (DQN). It addresses several limitations associate...

• Understanding Q-Values

  # Understanding Q-Values Q-Values, or action-value functions, are central to the Q-Learning algorithm and reinforcement learning in general. They represent the expected cumulative reward of taking a ...

• The Q-Learning Algorithm

  # The Q-Learning Algorithm Q-Learning is a model-free reinforcement learning algorithm to find the optimal action-selection policy for a given finite Markov decision process. It helps agents learn ho...

• DQN for Robotics and Control Systems

  # DQN for Robotics and Control Systems In the realm of robotics and control systems, Deep Q-Networks (DQN) have emerged as a powerful tool for enabling machines to learn from their interactions with ...

• Quiz: Real-world Applications of DQN

  # Real-world Applications of Deep Q-Networks (DQN) Deep Q-Networks (DQN) have revolutionized the field of reinforcement learning by enabling agents to learn optimal policies directly from high-dimens...

• And 15 more topics...