Complex-Valued-Reinforcement-Learning-course

The creative RL-modeling studio of the Neuverso Academy is pleased to welcome you to the first lesson of the Complex Valued Reinforcement Learning course.

Course Content:

• Advantages of CVRL.
• Complex-valued gradients in RL.
• Complex-valued functions in reinforcement learning.
• Representation of state-action values as complex numbers.
• Phase and amplitude in complex-valued RL.
• Extension of Q-values to the complex domain.
• Learning algorithm and update rules in complex-valued Q-learning.
• Action selection using complex Q-values.
• Complex-valued reward function.
• Learning algorithm for complex-valued reward shaping.
• Action selection in complex-valued reward shaping.
• Definition and issues of perceptual similarity.
• How complex-valued RL addresses the problem of perceptual similarity.
• Representation of context using phase information.
• Concept of the internal reference vector.
• Time-dependent phase function.
• Role in action selection and learning.
• Experimental evaluation of CVRL.
• Parameter tuning.
• Working with dynamic environments.
• Application to continuous state spaces.
• Extension to multidimensional spaces.
• Application of CVRL to real robotic tasks.
• Integration of CVRL with other machine learning methods.
• Autonomous robots in uncertain environments.
• Multi-agent systems in CVRL.

Welcome

Reinforcement learning (RL) is evolving, and complex-valued approaches open new horizons in machine learning. The Complex Valued Reinforcement Learning (CVRL) course will guide you through these innovative techniques. Whether you're working with dynamic environments, robotic systems, or multi-agent setups, CVRL introduces unique advantages in terms of handling state-action representations and perceptual similarities.

Who is this for:

This course is designed for researchers, students, and developers with an interest in machine learning, robotics, or reinforcement learning. Prior experience with RL concepts will be helpful but is not mandatory.

What you'll learn:

Throughout the course, you'll explore complex-valued gradients, reward shaping, and Q-values, and apply these concepts to real-world tasks such as autonomous systems and robotics.

What you'll build:

You'll implement advanced RL models using complex-valued data and create a repository of solutions to be applied in various applications, including multi-agent systems and continuous state spaces.

Prerequisites:

Basic knowledge of reinforcement learning and Python is recommended, but not mandatory.

How long:

This course is self-paced, allowing you to complete it at your own rhythm.

In this course, you will:

• Learn the theoretical foundations of CVRL
• Implement complex-valued Q-learning algorithms
• Apply CVRL to autonomous robots and multi-agent systems