Reinforcement Learning
A Reduction-based Framework for Sequential Decision Making with Delayed Feedback Y unchang Y ang 1 Han Zhong 1 Tianhao Wu2 Bin Liu 3
More examples include but are not limited to robotics (Mahmood et al.,
Supplementary Material A Data Modeling
In this section, we provide further details for our data modeling. We note the difficulties of appropriately modeling the terminal variable which is a binary variable compared to the rest of the dimensions which are continuous for the environments we investigate. This is particularly challenging for "expert" datasets where early termination is rare. An immediate advantage of sampling data from a generative model is compression. As we discuss in Appendix B.3, sampling is fast ER provides high levels of dataset compression without sacrificing downstream performance in offline 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.