Reinforcement Learning – Part II

Reinforcement Learning (RL) offers a framework for tackling sequential decision-making problems in areas like robotics and energy management. But how do you turn a real-world problem into a well-defined RL task?

In part two of this workshop series, we move from foundational RL concepts to practical application. In this interactive workshop, we use a simple illustrative example, such as managing an energy storage system, to demonstrate the step-by-step process of translating a given task into a RL setup.

You learn to assess when RL is suitable and, if so, how to design the environment by defining state and action spaces and reward functions, and how to implement it using Python's Gym library.

The goal is to introduce you to the workflow, show you how to evaluate and adjust your framework, and spark curiosity to tackle your own problems with RL.

Requirements

Basic knowledge of Python is recommended.
Basic RL knowledge is recommended (e.g. being familiar with these terms: agent, state, action, reward, policy).
Please bring a laptop to the workshop.

Registration & participation

Registration is binding. If you are unable to attend, please cancel your ticket in good time to allow other participants to take your place.

Important Information

Photographs, audio recordings and films will be made during the event. By participating in the event, you agree that photos, audio and video recordings in which you are recognizable may be published as part of the public relations work of the AI Service Center Berlin-Brandenburg and the HPI.

The is a project of the Federal Ministry of Research, Technology and Space. Its aim is to lower the barriers for using AI in business and society.

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