Scaling the Heights of Learning with Hierarchical Approaches in Reinforcement Learning

Authors: Harsha Vardhan Uckoo
Primary area: reinforcement learning
Keywords: Representation Learning, Reinforcement Learning, Temporal Abstraction, Cumulative Reward Optimization

Abstract:
This research explores a novel hierarchical representation learning framework designed to enhance planning and reinforcement learning (RL) in complex environments. By decoupling high-level decision-making from low-level control actions, our framework significantly improves sample efficiency and transfer learning performance across diverse tasks. We validate our approach through experiments in various environments, including Meta-World, AirSim, and Habitat AI, demonstrating that our hierarchical model consistently outperforms traditional flat models in cumulative rewards and adaptability to new tasks. This work lays the foundation for scalable AI systems capable of navigating the complexities of real-world applications.

Decision: reject

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