Reinforcement Learning
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.
Active Observing in Continuous-time Control
The decision-maker's observing policy must be timely
The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning
Offline reinforcement learning (RL) aims to train agents from pre-collected datasets.
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.
Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions
A notable example that motivates our work is the problem of minimizing costs (or maximizing rewards) in a single-trajectory Markov Decision Process (MDP).
