Hierarchical Reinforcement Learning
87 papers with code • 0 benchmarks • 2 datasets
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Hierarchical Reinforcement Learning for Power Network Topology Control
Whereas at the highest level a purely rule-based policy is still chosen for all agents in this study, at the intermediate level the policy is trained using different state-of-the-art RL algorithms.
Chain-of-Choice Hierarchical Policy Learning for Conversational Recommendation
Conversational Recommender Systems (CRS) illuminate user preferences via multi-round interactive dialogues, ultimately navigating towards precise and satisfactory recommendations.
Feature Interaction Aware Automated Data Representation Transformation
Creating an effective representation space is crucial for mitigating the curse of dimensionality, enhancing model generalization, addressing data sparsity, and leveraging classical models more effectively.
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based Rollout
Besides, we propose a one-step rollout-based planning to further facilitate inter-level cooperation, where the higher-level Q-function is used to guide the lower-level policy by estimating the value of future states so that global task information is transmitted downwards to avoid local pitfalls.
EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading
In stage II, we construct a pool of diverse RL agents for different market trends, distinguished by return rates, where hundreds of RL agents are trained with different preferences of return rates and only a tiny fraction of them will be selected into the pool based on their profitability.
Balancing Exploration and Exploitation in Hierarchical Reinforcement Learning via Latent Landmark Graphs
However, existing works often overlook the temporal coherence in GCHRL when learning latent subgoal representations and lack an efficient subgoal selection strategy that balances exploration and exploitation.
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDN
Although existing SDN intelligent solution methods, which are based on deep reinforcement learning, can dynamically adapt to complex network link state changes, these methods are plagued by problems such as redundant branches, large action space, and slow agent convergence.
A Hierarchical Approach to Population Training for Human-AI Collaboration
A major challenge for deep reinforcement learning (DRL) agents is to collaborate with novel partners that were not encountered by them during the training phase.
H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem
We propose an end-to-end learning framework based on hierarchical reinforcement learning, called H-TSP, for addressing the large-scale Travelling Salesman Problem (TSP).
Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation
Goal-conditioned hierarchical reinforcement learning (HRL) provides a promising direction to tackle this challenge by introducing a hierarchical structure to decompose the search space, where the low-level policy predicts primitive actions in the guidance of the goals derived from the high-level policy.