Delayed Homomorphic Reinforcement Learning for Environments with Delayed Feedback
Summary: arXiv:2604.03641v1 Announce Type: cross
Reinforcement learning (RL) has gained significant traction in various real-world applications, yet it often encounters challenges when dealing with delayed feedback. This phenomenon disrupts the Markov property, which is critical for effective learning and control in dynamic environments. In traditional reinforcement learning frameworks, this delay can lead to complications that hinder the agent’s ability to learn and adapt effectively.
Current state augmentation techniques are often employed to address delayed feedback issues. However, these methods frequently result in a state-space explosion, leading to increased sample complexity—a critical barrier that limits the practicality of RL in real-world scenarios. Despite advancements in this area, most augmentation-based approaches are still inadequate. They either focus primarily on alleviating the burden for the critic or implement disparate strategies for the actor and critic, which can further complicate the learning process.
Introducing Delayed Homomorphic Reinforcement Learning (DHRL)
To tackle these challenges, we introduce Delayed Homomorphic Reinforcement Learning (DHRL), a novel framework rooted in Markov Decision Process (MDP) homomorphisms. DHRL innovatively collapses belief-equivalent augmented states, facilitating efficient policy learning within a simplified abstract MDP while maintaining optimality.
Theoretical Underpinnings and Practical Applications
The DHRL framework is backed by comprehensive theoretical analyses that establish state-space compression bounds and sample complexity metrics. These insights are pivotal in demonstrating the efficiency and practicality of our approach. Furthermore, we have developed a practical algorithm that operationalizes these theoretical principles.
Experimental Validation
To validate the efficacy of DHRL, we conducted extensive experiments on continuous control tasks using the MuJoCo benchmark. The results indicate that our algorithm significantly outperforms existing augmentation-based baselines, especially in scenarios characterized by prolonged delays.
Key Findings
- DHRL effectively addresses the challenges posed by delayed feedback in reinforcement learning.
- The framework reduces sample complexity without compromising on optimality.
- Experimental results demonstrate superior performance over traditional augmentation-based methods.
- The approach is scalable and applicable to a range of real-world continuous control tasks.
Conclusion
In conclusion, Delayed Homomorphic Reinforcement Learning presents a structured, sample-efficient solution for environments plagued by delayed feedback. By leveraging MDP homomorphisms, DHRL not only simplifies the learning process but also enhances the agent’s ability to adapt and perform optimally in complex, dynamic settings. As reinforcement learning continues to evolve, frameworks like DHRL will play a crucial role in bridging the gap between theoretical advancements and practical applications.