Markov Models
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations
In Reinforcement Learning (RL), an agent engages with an environment in a sequential manner.
benchmarks (Freeman et al., 2021) show that T A
However, various systems are inherently continuous in time, making discrete-time MDPs an inexact modeling choice. In many applications, such as greenhouse control or medical treatments, each interaction (measurement or switching of action) involves manual intervention and thus is inherently costly.
Unscrambling disease progression at scale: fast inference of event permutations with optimal transport
Disease progression models infer group-level temporal trajectories of change in patients' features as a chronic degenerative condition plays out.
Active Observing in Continuous-time Control
The decision-maker's observing policy must be timely
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy Cameron Allen UC Berkeley Aaron Kirtland Brown University Ruo Y u Tao
These authors contributed equally and are ordered alphabetically.
Induced Model Matching: Restricted Models Help Train Full-Featured Models
This restricted model may be thought of as "side-information", and can come
Anormativetheoryofsocialconflict
Social conflict is a survival mechanism yielding both normal and pathological behaviors. Tounderstand its underlying principles, we collected behavioral and whole-brain neural data from mice advancing through stages of social conflict.
