06-06, 15:30–17:00 (Europe/London), Doddington Forum
Reinforcement Learning (RL) has emerged as a transformative sub-field in AI/ML, driving breakthroughs in areas ranging from autonomous robotics to personalized recommendation systems. This workshop is designed to serve a broad audience—from beginners eager to grasp foundational RL concepts to practitioners seeking to deepen their technical expertise through applied projects. These projects will range from developing simple classical RL game environments to practical financial domain use cases such as using RL sequential decision making for stock trading and asset portfolio optimization scenarios.
Over the course of this interactive session, participants will embark on a journey that begins with an introduction to the fundamental principles of RL, including Markov Decision Processes, reward structures, and the critical balance between exploration and exploitation. We will then transition into a series of hands-on coding exercises using popular frameworks such as Python’s Gymnasium (formally referred to as Gym), PyTorch and RL open-source libraries such as Stable-baselines3 and Machin (to name a few). These exercises will enable attendees to implement classic algorithms like Q-learning, SARSA and deep learning algorithms such as actor-critic architectures and policy gradients in controlled environments.
Real-world case studies and example use cases—ranging from classical simple simulated game environments to realistic decision-making systems in finance (such as stock trading and asset portfolio optimization use cases) - will illustrate how RL methodologies are applied in practice. During this workshop participants will develop and fine-tune RL models, gaining insights into performance evaluation, model tuning, and deployment strategies. Additionally, advanced topics such as deep RL architectures, on-policy and off-policy RL algorithms will be discussed and hacked interactively.
This workshop aims not only to impart theoretical knowledge but also to empower participants with the practical skills needed to design and deploy effective RL solutions. Join us to explore the dynamic world of reinforcement learning and to enhance your toolkit for solving complex, data-driven challenges. All the python libraries/packages, reference papers and data used in this workshop will be open sourced and made available in a Github repo (which will be made available soon).
No previous knowledge expected
A lead software engineer and data scientist. Has over 15 years’ experience in the development of software and AI/ML solutions. Pragmatic, analytic problem solver and builder of artificial intelligence solutions for business seeking efficiency and value. A passionate advocate of the development and use of ethical AI in products and services.