Reinforcement Learning (RL) has achieved many successes over the years in training autonomous agents to perform simple tasks. However, one of the major remaining challenges in RL is scaling it to high-dimensional, real-world applications.
Although many works have already focused on strategies to scale-up RL techniques and to find solutions for more complex problems with reasonable successes, many issues still exist. This workshop encourages to discuss diverse approaches to accelerate and generalize RL, such as the use of approximations, abstractions, hierarchical approaches, and Transfer Learning.
Scaling-up RL methods has major implications on the research and practice of complex learning problems and will eventually lead to successful implementations in real-world applications.
This workshop intends to bridge the gap between conventional and scalable RL approaches. We aim to bring together researchers working on different approaches to scale-up RL with the goal to solve more complex or larger scale problems. We intend to make this an exciting event for researchers worldwide, not only for the presentation of top quality papers, but also to spark the discussion of opportunities and challenges for future research directions.
Examples of covered topics include (but are not limited to)
If you want to get in contact with the organization committee of the workshop please use the contact form provided here.