ICML 2024 "markov decision processes" Papers
9 papers found
AI Alignment with Changing and Influenceable Reward Functions
Micah Carroll, Davis Foote, Anand Siththaranjan et al.
ICML 2024posterarXiv:2405.17713
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling
Danil Provodin, Maurits Kaptein, Mykola Pechenizkiy
ICML 2024posterarXiv:2405.19017
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
Riccardo De Santi, Federico Arangath Joseph, Noah Liniger et al.
ICML 2024posterarXiv:2407.13364
Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
Lei Zhao, Mengdi Wang, Yu Bai
ICML 2024posterarXiv:2312.00054
Model-Free Robust $\phi$-Divergence Reinforcement Learning Using Both Offline and Online Data
Kishan Panaganti, Adam Wierman, Eric Mazumdar
ICML 2024poster
On The Statistical Complexity of Offline Decision-Making
Thanh Nguyen-Tang, Raman Arora
ICML 2024posterarXiv:2501.06339
SaVeR: Optimal Data Collection Strategy for Safe Policy Evaluation in Tabular MDP
Subhojyoti Mukherjee, Josiah Hanna, Robert Nowak
ICML 2024posterarXiv:2406.02165
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation
Fengdi Che, Chenjun Xiao, Jincheng Mei et al.
ICML 2024oralarXiv:2405.21043
Test-Time Regret Minimization in Meta Reinforcement Learning
Mirco Mutti, Aviv Tamar
ICML 2024posterarXiv:2406.02282