Hierarchical Reinforcement Learning
87 papers with code • 0 benchmarks • 2 datasets
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Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning
For pair selection, ignoring the trading performance results in the wrong assets being selected with irrelevant price movements, while the agent trained for trading can overfit to the selected assets without any historical information of other assets.
METEOR Guided Divergence for Video Captioning
Using our BMHRL, we show the suitability of the HRL agent in the generation of content-complete and grammatically sound sentences by achieving $4. 91$, $2. 23$, and $10. 80$ in BLEU3, BLEU4, and METEOR scores, respectively on the ActivityNet Captions dataset.
DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning
Hierarchical Reinforcement Learning (HRL) has made notable progress in complex control tasks by leveraging temporal abstraction.
Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning
Motivated by this, we investigate developing a learning-based over-sampling algorithm to optimize the classification performance, which is a challenging task because of the huge and hierarchical decision space.
From Scratch to Sketch: Deep Decoupled Hierarchical Reinforcement Learning for Robotic Sketching Agent
We present an automated learning framework for a robotic sketching agent that is capable of learning stroke-based rendering and motor control simultaneously.
Step by step: a hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning
Due to this one-to-many dilemma, enlarged action space and ignoring logical relationship between entity and relation increase the difficulty of learning.
Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential Recommendation (SCSR) is an emerging yet challenging task that simultaneously considers the shared-account and cross-domain characteristics in the sequential recommendation.
Deep Hierarchical Planning from Pixels
Despite operating in latent space, the decisions are interpretable because the world model can decode goals into images for visualization.
Possibility Before Utility: Learning And Using Hierarchical Affordances
Existing works in hierarchical reinforcement learning provide agents with structural representations of subtasks but are not affordance-aware, and by grounding our definition of hierarchical affordances in the present state, our approach is more flexible than the multitude of approaches that ground their subtask dependencies in a symbolic history.
On Credit Assignment in Hierarchical Reinforcement Learning
To improve our fundamental understanding of HRL, we investigate hierarchical credit assignment from the perspective of conventional multistep reinforcement learning.