When demonstrations meet generative world models: A maximum likelihood framework for offline inverse reinforcement learning
Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards
and environment dynamics that underlie observed actions in a fixed, finite set of …
and environment dynamics that underlie observed actions in a fixed, finite set of …
Understanding expertise through demonstrations: A maximum likelihood framework for offline inverse reinforcement learning
Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards
and environment dynamics that underlie observed actions in a fixed, finite set of …
and environment dynamics that underlie observed actions in a fixed, finite set of …
Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
In inverse reinforcement learning (IRL), an agent seeks to replicate expert demonstrations
through interactions with the environment. Traditionally, IRL is treated as an adversarial …
through interactions with the environment. Traditionally, IRL is treated as an adversarial …
OMG-RL: Offline Model-based Guided Reward Learning for Heparin Treatment
Y Lim, S Lee - arxiv preprint arxiv:2409.13299, 2024 - arxiv.org
Accurate diagnosis of individual patient conditions and appropriate medication dosing
strategies are core elements of personalized medical decision-making processes. This …
strategies are core elements of personalized medical decision-making processes. This …
A Survey of Current Applications of Inverse Reinforcement Learning in Aviation and Future Outlooks
Many problems in aviation can be characterized as sequential decision-making problems
under uncertainty, such as air traffic management and flight delay prediction. One approach …
under uncertainty, such as air traffic management and flight delay prediction. One approach …