Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning
Authors: Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, Zhaoran Wang
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also provide empirical studies to demonstrate the efficacy of the UCB-based contrastive learning method for RL. |
| Researcher Affiliation | Collaboration | 1University of Chicago. 2Northwestern University. 3Shanghai AI Laboratory. 4Yale University. |
| Pseudocode | Yes | Algorithm 1 Online Contrastive RL for Single-Agent MDPs |
| Open Source Code | Yes | The codes are available at https://github.com/Baichenjia/Contrastive-UCB. |
| Open Datasets | Yes | In our experiments, we use Atari 100K (Kaiser et al., 2020) benchmark for evaluation... |
| Dataset Splits | No | The paper refers to a 'training stage' and 'testing' of the algorithms, and uses the Atari 100K benchmark, but does not explicitly provide numerical details or methodology for training/test/validation dataset splits within its text. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory, or cloud instance types) used to run its experiments. |
| Software Dependencies | No | The paper discusses adopting the 'SPR method' and its architecture but does not specify software dependencies like programming languages or libraries with their version numbers. |
| Experiment Setup | Yes | In particular, we adopt the same hyper-parameters as that of SPR (Schwarzer et al., 2021)." and "Meanwhile, we adopt the last layer of the Q-network as our learned representation bϕ which is linear in the estimated Q-function... The bonus for the state-action pair (s, a) is calculated by βk(s, a) = γk [bϕ(s, a) (bΣk h) 1 bϕ(s, a)] 1 2 , where we set the hyperparameter γk = 1 for all iterations k [K]. |