The Value of Reward Lookahead in Reinforcement Learning
Authors: Nadav Merlis, Dorian Baudry, Vianney Perchet
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we aim to quantifiably analyze the value of such future reward information through the lens of competitive analysis. In particular, we measure the ratio between the value of standard RL agents and that of agents with partial future-reward lookahead. We characterize the worst-case reward distribution and derive exact ratios for the worst-case reward expectations. Surprisingly, the resulting ratios relate to known quantities in offline RL and reward-free exploration. We further provide tight bounds for the ratio given the worst-case dynamics. |
| Researcher Affiliation | Collaboration | Nadav Merlis Fair Play Joint Team, CREST, ENSAE Paris nadav.merlis@ensae.fr Dorian Baudry Fair Play Joint Team, CREST, ENSAE Paris Institut Polytechnique de Paris Vianney Perchet Fair Play Joint Team, CREST, ENSAE Paris Criteo AI Lab |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code. There is no mention of code release or repository links. |
| Open Datasets | No | The paper does not provide concrete access information for a publicly available or open dataset, as it is a theoretical paper. |
| Dataset Splits | No | The paper does not provide specific dataset split information, as it is a theoretical paper. |
| Hardware Specification | No | The paper does not provide specific hardware details, as it is a theoretical paper and does not report on experiments requiring hardware. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, as it is a theoretical paper and does not report on experiments requiring software dependencies. |
| Experiment Setup | No | The paper does not contain specific experimental setup details, as it is a theoretical paper and does not report on experiments or their setup. |