Scaling the Heights of Learning with Hierarchical Approaches in Reinforcement Learning
Representation Learning;Reinforcement Learning;Temporal Abstraction;Cumulative Reward Optimization
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We propose a representation learning model that differentiates short-term actions from long-term goals, optimizing cumulative rewards and enabling efficient transfer learning in reinforcement learning.
@misc{
uckoo2024scaling,
title={Scaling the Heights of Learning with Hierarchical Approaches in Reinforcement Learning},
author={Harsha Vardhan Uckoo},
year={2024},
url={https://openreview.net/forum?id=L5gZcmaHB7}
}
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.
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Harsha Vardhan Uckoo
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Representation Learning Reinforcement Learning Temporal Abstraction Cumulative Reward Optimization
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uckoo|scaling_the_heights_of_learning_with_hierarchical_approaches_in_reinforcement_learning
reinforcement learning
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Scaling the Heights of Learning with Hierarchical Approaches in Reinforcement Learning
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