Hierarchical Reinforcement Learning

87 papers with code • 0 benchmarks • 2 datasets

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Use these libraries to find Hierarchical Reinforcement Learning models and implementations
3 papers
48

Hierarchical Reinforcement Learning for Power Network Topology Control

bmanczak/runPowerNetworks 3 Nov 2023

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.

5
03 Nov 2023

Chain-of-Choice Hierarchical Policy Learning for Conversational Recommendation

alexfanw/cochpl 27 Oct 2023

Conversational Recommender Systems (CRS) illuminate user preferences via multi-round interactive dialogues, ultimately navigating towards precise and satisfactory recommendations.

2
27 Oct 2023

Feature Interaction Aware Automated Data Representation Transformation

ehtesam3154/inhrecon 29 Sep 2023

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.

0
29 Sep 2023

Guided Cooperation in Hierarchical Reinforcement Learning via Model-based Rollout

haoranwang-tj/gcmr_aclg_official 24 Sep 2023

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.

1
24 Sep 2023

EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading

qinmoelei/EarnHFT 22 Sep 2023

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.

32
22 Sep 2023

Balancing Exploration and Exploitation in Hierarchical Reinforcement Learning via Latent Landmark Graphs

papercode2022/hill 22 Jul 2023

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.

2
22 Jul 2023

DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDN

guetye/dhrl-fnmr 30 May 2023

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.

2
30 May 2023

A Hierarchical Approach to Population Training for Human-AI Collaboration

marvl-hipt/hipt 26 May 2023

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.

0
26 May 2023

H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem

Learning4Optimization-HUST/H-TSP 19 Apr 2023

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).

23
19 Apr 2023

Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation

yang-xy20/mage-x 8 Feb 2023

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.

4
08 Feb 2023