In this paper, we delve into the intricacies of real-time reinforcement learning (RTRL) and its limitations in exploring optimal solutions. RTRL algorithms aim to learn from experiences gathered over time, with the goal of maximizing the cumulative reward earned. However, as the environment evolves, the ability to adapt and explore new strategies becomes crucial.
To better understand the challenges faced by RTRL algorithms, we introduce the concept of an "effective horizon," which represents the maximum amount of time an algorithm can consider when making decisions. This horizon grows exponentially with the complexity of the problem, threatening to overwhelm even the most advanced RTRL methods.
To combat this issue, we propose a new approach called SQIRL, which combines elements of both model-based and model-free RTRL techniques. By leveraging the strengths of both paradigms, SQIRL is able to balance exploration and exploitation more effectively than previous methods.
We demonstrate the efficacy of SQIRL through a series of experiments, showing that it can learn optimal policies in complex environments with a manageable effective horizon. Our results suggest that SQIRL offers a promising solution for RTRL problems, allowing agents to adapt and learn in real-time while avoiding the curse of dimensionality.
In summary, this paper sheds light on the inherent limitations of RTRL algorithms and proposes a novel approach that addresses these challenges by combining elements of model-based and model-free techniques. By effectively balancing exploration and exploitation, SQIRL demonstrates improved performance in complex environments, paving the way for more sophisticated RTRL methods in the future.
Algorithmic Insights into Stochastic Recommendation Systems
