Planning with General Objective Functions: Going Beyond Total Rewards
Authors: Ruosong Wang, Peilin Zhong, Simon S. Du, Russ R. Salakhutdinov, Lin Yang
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work is mainly theoretical. By devising provably efficient algorithms for planning with general objective functions, we believe our various algorithmic insights (discretization, augmenting state space) could potentially guide practitioners to design efficient and theoretically-principled planning algorithms that work for various settings. |
| Researcher Affiliation | Academia | Ruosong Wang Carnegie Mellon University ruosongw@andrew.cmu.edu Peilin Zhong Columbia University pz2225@columbia.edu Simon S. Du University of Washington, Seattle ssdu@cs.washington.edu Ruslan Salakhutdinov Carnegie Mellon University rsalakhu@cs.cmu.edu Lin F. Yang University of California, Los Angeles linyang@ee.ucla.edu |
| Pseudocode | Yes | Algorithm 1 Deterministic Systems with General Reward Functions |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a dataset, hence no mention of publicly available datasets for training. |
| Dataset Splits | No | As a theoretical paper, it does not involve data splitting for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |