Rank-1 Approximation of Inverse Fisher for Natural Policy Gradients in Deep Reinforcement Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Huo, Yingxiao, Dash, Satya Prakash, Stoican, Radu, Kaski, Samuel, Sun, Mingfei |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Gradient Regularized Natural Gradients
by: Dash, Satya Prakash, et al.
Published: (2026)
by: Dash, Satya Prakash, et al.
Published: (2026)
Randomized Advantage Transformation (RAT): Computing Natural Policy Gradients via Direct Backpropagation
by: Sun, Mingfei
Published: (2026)
by: Sun, Mingfei
Published: (2026)
Bayesian Natural Gradient Fine-Tuning of CLIP Models via Kalman Filtering
by: Abdi, Hossein, et al.
Published: (2025)
by: Abdi, Hossein, et al.
Published: (2025)
Matrix Low-Rank Approximation For Policy Gradient Methods
by: Rozada, Sergio, et al.
Published: (2024)
by: Rozada, Sergio, et al.
Published: (2024)
Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning
by: Zhang, Beining, et al.
Published: (2025)
by: Zhang, Beining, et al.
Published: (2025)
Fisher-Orthogonal Projected Natural Gradient Descent for Continual Learning
by: Garg, Ishir, et al.
Published: (2026)
by: Garg, Ishir, et al.
Published: (2026)
Optimal Policy Sparsification and Low Rank Decomposition for Deep Reinforcement Learning
by: Goddla, Vikram
Published: (2024)
by: Goddla, Vikram
Published: (2024)
TROFI: Trajectory-Ranked Offline Inverse Reinforcement Learning
by: Sestini, Alessandro, et al.
Published: (2025)
by: Sestini, Alessandro, et al.
Published: (2025)
Recursive Deep Inverse Reinforcement Learning
by: Ghanem, Paul, et al.
Published: (2025)
by: Ghanem, Paul, et al.
Published: (2025)
Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning
by: Yang, Tong, et al.
Published: (2023)
by: Yang, Tong, et al.
Published: (2023)
The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations
by: Lehmann, Matthias
Published: (2024)
by: Lehmann, Matthias
Published: (2024)
Towards Off-Policy Reinforcement Learning for Ranking Policies with Human Feedback
by: Xiao, Teng, et al.
Published: (2024)
by: Xiao, Teng, et al.
Published: (2024)
The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks
by: Mayor, Walter, et al.
Published: (2025)
by: Mayor, Walter, et al.
Published: (2025)
Fisher-Guided Selective Forgetting: Mitigating The Primacy Bias in Deep Reinforcement Learning
by: Falzari, Massimiliano, et al.
Published: (2025)
by: Falzari, Massimiliano, et al.
Published: (2025)
Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning
by: Xie, Sean, et al.
Published: (2022)
by: Xie, Sean, et al.
Published: (2022)
Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning
by: Rozada, Sergio, et al.
Published: (2022)
by: Rozada, Sergio, et al.
Published: (2022)
In-Context Black-Box Optimization with Unreliable Feedback
by: Blumer, Nicolas Samuel, et al.
Published: (2026)
by: Blumer, Nicolas Samuel, et al.
Published: (2026)
Policy Gradient Methods for Non-Markovian Reinforcement Learning
by: Kar, Avik, et al.
Published: (2026)
by: Kar, Avik, et al.
Published: (2026)
Policy Gradients for Cumulative Prospect Theory in Reinforcement Learning
by: Lepel, Olivier, et al.
Published: (2024)
by: Lepel, Olivier, et al.
Published: (2024)
Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach
by: Xu, Yang, et al.
Published: (2025)
by: Xu, Yang, et al.
Published: (2025)
Deep Reinforcement Learning with Gradient Eligibility Traces
by: Elelimy, Esraa, et al.
Published: (2025)
by: Elelimy, Esraa, et al.
Published: (2025)
Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning
by: Zhuang, Yuan, et al.
Published: (2026)
by: Zhuang, Yuan, et al.
Published: (2026)
Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension
by: Gong, Wenbo, et al.
Published: (2025)
by: Gong, Wenbo, et al.
Published: (2025)
Beyond the Mean: Fisher-Orthogonal Projection for Natural Gradient Descent in Large Batch Training
by: Lu, Yishun, et al.
Published: (2025)
by: Lu, Yishun, et al.
Published: (2025)
Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies
by: Tayal, Mumuksh, et al.
Published: (2026)
by: Tayal, Mumuksh, et al.
Published: (2026)
Transformer Normalisation Layers and the Independence of Semantic Subspaces
by: Menary, Stephen, et al.
Published: (2024)
by: Menary, Stephen, et al.
Published: (2024)
Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying
by: Nishimori, Soichiro, et al.
Published: (2026)
by: Nishimori, Soichiro, et al.
Published: (2026)
$K$-Level Policy Gradients for Multi-Agent Reinforcement Learning
by: Reddi, Aryaman, et al.
Published: (2025)
by: Reddi, Aryaman, et al.
Published: (2025)
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
by: Batra, Sumeet, et al.
Published: (2023)
by: Batra, Sumeet, et al.
Published: (2023)
Gradient Free Deep Reinforcement Learning With TabPFN
by: Schiff, David, et al.
Published: (2025)
by: Schiff, David, et al.
Published: (2025)
Rethinking Policy Diversity in Ensemble Policy Gradient in Large-Scale Reinforcement Learning
by: Shitanda, Naoki, et al.
Published: (2026)
by: Shitanda, Naoki, et al.
Published: (2026)
Stabilizing Policy Gradients for Sample-Efficient Reinforcement Learning in LLM Reasoning
by: Melo, Luckeciano C., et al.
Published: (2025)
by: Melo, Luckeciano C., et al.
Published: (2025)
On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning
by: Barakat, Anas, et al.
Published: (2024)
by: Barakat, Anas, et al.
Published: (2024)
PG-Rainbow: Using Distributional Reinforcement Learning in Policy Gradient Methods
by: Jeon, WooJae, et al.
Published: (2024)
by: Jeon, WooJae, et al.
Published: (2024)
Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning
by: Sun, Hao, et al.
Published: (2024)
by: Sun, Hao, et al.
Published: (2024)
Supervised Fine-Tuning as Inverse Reinforcement Learning
by: Sun, Hao
Published: (2024)
by: Sun, Hao
Published: (2024)
Performative Policy Gradient: Optimality in Performative Reinforcement Learning
by: Basu, Debabrota, et al.
Published: (2025)
by: Basu, Debabrota, et al.
Published: (2025)
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
by: Panangaden, Prakash, et al.
Published: (2023)
by: Panangaden, Prakash, et al.
Published: (2023)
A Survey of State Representation Learning for Deep Reinforcement Learning
by: Echchahed, Ayoub, et al.
Published: (2025)
by: Echchahed, Ayoub, et al.
Published: (2025)
Hybrid Inverse Reinforcement Learning
by: Ren, Juntao, et al.
Published: (2024)
by: Ren, Juntao, et al.
Published: (2024)
Similar Items
-
Gradient Regularized Natural Gradients
by: Dash, Satya Prakash, et al.
Published: (2026) -
Randomized Advantage Transformation (RAT): Computing Natural Policy Gradients via Direct Backpropagation
by: Sun, Mingfei
Published: (2026) -
Bayesian Natural Gradient Fine-Tuning of CLIP Models via Kalman Filtering
by: Abdi, Hossein, et al.
Published: (2025) -
Matrix Low-Rank Approximation For Policy Gradient Methods
by: Rozada, Sergio, et al.
Published: (2024) -
Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning
by: Zhang, Beining, et al.
Published: (2025)