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..
Generalizing Reinforcement Learning through Fusing Self-Supervised Learning into Intrinsic Motivation
Authors: Keyu Wu, Min Wu, Zhenghua Chen, Yuecong Xu, Xiaoli Li8683-8690
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations have been performed on a diversity of tasks. Experimental results demonstrate that SIM consistently outperforms the state-of-the-art methods and exhibits superior generalization capability and sample efficiency. |
| Researcher Affiliation | Collaboration | Institute for Infocomm Research , A*STAR, Singapore EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: SIM |
| Open Source Code | Yes | Our code and more experimental results are available at https://github.com/Kerry Wu16/SIM. |
| Open Datasets | Yes | Deep Mind Control Suite (DMControl) (Tassa et al. 2018) is a widely used benchmark dataset for vision-based RL algorithm comparison. |
| Dataset Splits | No | The paper does not explicitly provide specific training, validation, and testing dataset splits by percentages or counts. It refers to 'training environment' and 'unseen environments' for evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or processor types used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Adam' as an optimizer but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Table 1: Hyperparameters used for the DMControl experiments. Observation rendering 100x100, Observation downsampling 84x84, Stacked frames 3, Action repeat 2 (finger) 8 (cartpole) 4 (otherwise), Discount factor γ 0.99, Replay buffer size 500,000, Initial steps 1000, Learning rate (actor, critic, SSL) 1e-3, Learning rate (α) 1e-4, Initial temperature 0.1, Trade-off constant λ 3.9e-3, Update frequency (actor, critic target, SSL) 2. |