Hierarchical Reinforcement Learning
87 papers with code • 0 benchmarks • 2 datasets
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Libraries
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Gibson Environment
bipedal-skills
Latest papers with no code
PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
In this work, we introduce PIPER: Primitive-Informed Preference-based Hierarchical reinforcement learning via Hindsight Relabeling, a novel approach that leverages preference-based learning to learn a reward model, and subsequently uses this reward model to relabel higher-level replay buffers.
Joint Optimization on Uplink OFDMA and MU-MIMO for IEEE 802.11ax: Deep Hierarchical Reinforcement Learning Approach
In unsaturated traffic conditions, considering packet volumes per user introduces a combinatorial problem, requiring the simultaneous optimization of MU-MIMO user selection and RA along the time-frequency-space axis.
Reinforcement Learning with Options and State Representation
The current thesis aims to explore the reinforcement learning field and build on existing methods to produce improved ones to tackle the problem of learning in high-dimensional and complex environments.
SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition
Instead of making behavioral decisions directly from the exponentially expanding joint observational-action space, subtask-based multi-agent reinforcement learning (MARL) methods enable agents to learn how to tackle different subtasks.
Explainable Session-based Recommendation via Path Reasoning
This paper explores providing explainability for session-based recommendation (SR) by path reasoning.
MENTOR: Guiding Hierarchical Reinforcement Learning with Human Feedback and Dynamic Distance Constraint
To address the issue, We propose a general hierarchical reinforcement learning framework incorporating human feedback and dynamic distance constraints (MENTOR).
Scaling Artificial Intelligence for Digital Wargaming in Support of Decision-Making
In this unprecedented era of technology-driven transformation, it becomes more critical than ever that we aggressively invest in developing robust artificial intelligence (AI) for wargaming in support of decision-making.
Scaling Intelligent Agents in Combat Simulations for Wargaming
Remaining competitive in future conflicts with technologically-advanced competitors requires us to accelerate our research and development in artificial intelligence (AI) for wargaming.
TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments
Additionally, TopoNav incorporates intrinsic motivation to guide exploration toward relevant regions and frontier nodes in the topological map, addressing the challenges of sparse extrinsic rewards.
Emergency Computing: An Adaptive Collaborative Inference Method Based on Hierarchical Reinforcement Learning
In this paper, we propose an Emergency Network with Sensing, Communication, Computation, Caching, and Intelligence (E-SC3I).
