Group Fairness in Multi-Task Reinforcement Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Song, Kefan, Jiang, Runnan, Chandra, Rohan, Zhang, Shangtong |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Models
by: Song, Kefan, et al.
Published: (2025)
by: Song, Kefan, et al.
Published: (2025)
Reward Is Enough: LLMs Are In-Context Reinforcement Learners
by: Song, Kefan, et al.
Published: (2025)
by: Song, Kefan, et al.
Published: (2025)
Experience Replay Addresses Loss of Plasticity in Continual Learning
by: Wang, Jiuqi, et al.
Published: (2025)
by: Wang, Jiuqi, et al.
Published: (2025)
Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features
by: Xie, Zixuan, et al.
Published: (2025)
by: Xie, Zixuan, et al.
Published: (2025)
Counterfactual Explanations for Continuous Action Reinforcement Learning
by: Dong, Shuyang, et al.
Published: (2025)
by: Dong, Shuyang, et al.
Published: (2025)
Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey
by: Jiang, Jiechuan, et al.
Published: (2024)
by: Jiang, Jiechuan, et al.
Published: (2024)
The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
by: Liu, Shuze Daniel, et al.
Published: (2024)
by: Liu, Shuze Daniel, et al.
Published: (2024)
Towards Provable Emergence of In-Context Reinforcement Learning
by: Wang, Jiuqi, et al.
Published: (2025)
by: Wang, Jiuqi, et al.
Published: (2025)
Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features
by: Wang, Jiuqi, et al.
Published: (2024)
by: Wang, Jiuqi, et al.
Published: (2024)
FairMT: Fairness for Heterogeneous Multi-Task Learning
by: Hu, Guanyu, et al.
Published: (2025)
by: Hu, Guanyu, et al.
Published: (2025)
Adaptive Policy Selection and Fine-Tuning under Interaction Budgets for Offline-to-Online Reinforcement Learning
by: Bozkurt, Alper Kamil, et al.
Published: (2026)
by: Bozkurt, Alper Kamil, et al.
Published: (2026)
Linear $Q$-Learning Does Not Diverge in $L^2$: Convergence Rates to a Bounded Set
by: Liu, Xinyu, et al.
Published: (2025)
by: Liu, Xinyu, et al.
Published: (2025)
Almost Sure Convergence of Differential Temporal Difference Learning for Average Reward Markov Decision Processes
by: Blaser, Ethan, et al.
Published: (2026)
by: Blaser, Ethan, et al.
Published: (2026)
FairDICE: Fairness-Driven Offline Multi-Objective Reinforcement Learning
by: Kim, Woosung, et al.
Published: (2025)
by: Kim, Woosung, et al.
Published: (2025)
Convergence and Emergence of In-Context Reinforcement Learning with Chain of Thought
by: Xie, Zixuan, et al.
Published: (2026)
by: Xie, Zixuan, et al.
Published: (2026)
Task Scheduling & Forgetting in Multi-Task Reinforcement Learning
by: Speckmann, Marc, et al.
Published: (2025)
by: Speckmann, Marc, et al.
Published: (2025)
Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness
by: Yuan, Yingfang, et al.
Published: (2024)
by: Yuan, Yingfang, et al.
Published: (2024)
Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds
by: Chandra, Rohan, et al.
Published: (2024)
by: Chandra, Rohan, et al.
Published: (2024)
Are LLMs The Way Forward? A Case Study on LLM-Guided Reinforcement Learning for Decentralized Autonomous Driving
by: Anvar, Timur, et al.
Published: (2025)
by: Anvar, Timur, et al.
Published: (2025)
Task-Aware Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning
by: Fan, Ziqing, et al.
Published: (2024)
by: Fan, Ziqing, et al.
Published: (2024)
Asymptotic and Finite Sample Analysis of Nonexpansive Stochastic Approximations with Markovian Noise
by: Blaser, Ethan, et al.
Published: (2024)
by: Blaser, Ethan, et al.
Published: (2024)
Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance
by: He, Jinmin, et al.
Published: (2025)
by: He, Jinmin, et al.
Published: (2025)
Multi-Task Reinforcement Learning Enables Parameter Scaling
by: McLean, Reginald, et al.
Published: (2025)
by: McLean, Reginald, et al.
Published: (2025)
Sparsity-Driven Plasticity in Multi-Task Reinforcement Learning
by: Todorov, Aleksandar, et al.
Published: (2025)
by: Todorov, Aleksandar, et al.
Published: (2025)
Probabilistic Performance Guarantees for Multi-Task Reinforcement Learning
by: Schnitzer, Yannik, et al.
Published: (2026)
by: Schnitzer, Yannik, et al.
Published: (2026)
Projected Task-Specific Layers for Multi-Task Reinforcement Learning
by: Roberts, Josselin Somerville, et al.
Published: (2023)
by: Roberts, Josselin Somerville, et al.
Published: (2023)
Multi-Agent Reinforcement Learning for Dynamic Pricing: Balancing Profitability,Stability and Fairness
by: Amma, Krishna Kumar Neelakanta Pillai Santha Kumari
Published: (2026)
by: Amma, Krishna Kumar Neelakanta Pillai Santha Kumari
Published: (2026)
Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning
by: Zhang, Kehao, et al.
Published: (2026)
by: Zhang, Kehao, et al.
Published: (2026)
Behavior-Invariant Task Representation Learning with Transformer-based World Models for Offline Meta-Reinforcement Learning
by: Qian, Fuyuan, et al.
Published: (2026)
by: Qian, Fuyuan, et al.
Published: (2026)
Soft Conflict-Resolution Decision Transformer for Offline Multi-Task Reinforcement Learning
by: Wang, Shudong, et al.
Published: (2025)
by: Wang, Shudong, et al.
Published: (2025)
Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning
by: Hu, Yaowei, et al.
Published: (2024)
by: Hu, Yaowei, et al.
Published: (2024)
Dual-Balancing for Multi-Task Learning
by: Lin, Baijiong, et al.
Published: (2023)
by: Lin, Baijiong, et al.
Published: (2023)
Goal-Oriented Skill Abstraction for Offline Multi-Task Reinforcement Learning
by: He, Jinmin, et al.
Published: (2025)
by: He, Jinmin, et al.
Published: (2025)
Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions
by: Abate, Alessandro, et al.
Published: (2026)
by: Abate, Alessandro, et al.
Published: (2026)
Efficient Multi-Task Reinforcement Learning via Task-Specific Action Correction
by: Feng, Jinyuan, et al.
Published: (2024)
by: Feng, Jinyuan, et al.
Published: (2024)
DMTG: One-Shot Differentiable Multi-Task Grouping
by: Gao, Yuan, et al.
Published: (2024)
by: Gao, Yuan, et al.
Published: (2024)
CALM: Consensus-Aware Localized Merging for Multi-Task Learning
by: Yan, Kunda, et al.
Published: (2025)
by: Yan, Kunda, et al.
Published: (2025)
The Composite Task Challenge for Cooperative Multi-Agent Reinforcement Learning
by: Li, Yurui, et al.
Published: (2025)
by: Li, Yurui, et al.
Published: (2025)
When should we prefer Decision Transformers for Offline Reinforcement Learning?
by: Bhargava, Prajjwal, et al.
Published: (2023)
by: Bhargava, Prajjwal, et al.
Published: (2023)
Centralized Reward Agent for Knowledge Sharing and Transfer in Multi-Task Reinforcement Learning
by: Ma, Haozhe, et al.
Published: (2024)
by: Ma, Haozhe, et al.
Published: (2024)
Similar Items
-
Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Models
by: Song, Kefan, et al.
Published: (2025) -
Reward Is Enough: LLMs Are In-Context Reinforcement Learners
by: Song, Kefan, et al.
Published: (2025) -
Experience Replay Addresses Loss of Plasticity in Continual Learning
by: Wang, Jiuqi, et al.
Published: (2025) -
Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features
by: Xie, Zixuan, et al.
Published: (2025) -
Counterfactual Explanations for Continuous Action Reinforcement Learning
by: Dong, Shuyang, et al.
Published: (2025)