Research map / Reinforcement learning / MDPs and planning
Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use hierarchical LMDP learning instead of flat Z-IS | Proposed hierarchical LMDP · Rooms Domain | self-normalized MAE | Helped |
| Use hierarchical LMDP learning instead of options-based Q_o | Proposed hierarchical LMDP · Rooms Domain | self-normalized MAE | Helped |
| Use hierarchical LMDP learning instead of flat Z-IS | Proposed hierarchical LMDP · Taxi Domain | self-normalized MAE | Helped |
| Use hierarchical LMDP learning instead of options-based Q_o | Proposed hierarchical LMDP · Taxi Domain | self-normalized MAE | Helped |
| Use update variant V3 instead of V1 or V2 (ablation) | Proposed hierarchical LMDP · Rooms Domain | self-normalized MAE | Helped |
| Use update variant V3 instead of V1 or V2 (ablation) | Proposed hierarchical LMDP · Taxi Domain | self-normalized MAE | No clear effect |
Closest papers by meaning
- On Efficiency in Hierarchical Reinforcement Learning (NeurIPS 2020 · similarity 0.90)
- An Efficient Approach to Model-Based Hierarchical Reinforcement Learning (AAAI 2017 · similarity 0.89)
- Hierarchy Through Composition with Multitask LMDPs (ICML 2017 · similarity 0.89)
- Oracle-Efficient Reinforcement Learning for Max Value Ensembles (NeurIPS 2024 · similarity 0.89)
- Hierarchical Reinforcement Learning with Uncertainty-Guided Diffusional Subgoals (ICML 2025 · similarity 0.88)
- Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy (NeurIPS 2024 · similarity 0.88)
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