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..
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
Authors: Cédric Colas, Pierre Fournier, Mohamed Chetouani, Olivier Sigaud, Pierre-Yves Oudeyer
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted in a new modular-goal robotic environment show the resulting developmental self-organization of a learning curriculum, and demonstrate properties of robustness to distracting goals, forgetting and changes in body properties. |
| Researcher Affiliation | Academia | 1Flowers Team, Inria and Ensta Paris Tech, FR. 2ISIR, Sorbonne Univ., Paris, FR. |
| Pseudocode | No | The detailed algorithm is given in the supplementary document. |
| Open Source Code | Yes | Links. The environment, code and video of the CURIOUS agent are made available at https://github.com/ flowersteam/curious. |
| Open Datasets | No | The paper describes creating a "new simulated environment adapted from the Open AI Gym suite" (Modular Goal Fetch Arm) rather than using a pre-existing publicly available dataset with specific access information. |
| Dataset Splits | No | The paper describes |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the simulations or train the models. |
| Software Dependencies | No | The paper mentions "Open AI Gym suite", "DDPG", "TD3", and "DQN" but does not specify version numbers for these software components or libraries. |
| Experiment Setup | Yes | The agent controls the position of its gripper and the gripper opening (4D). ... p = 0.8 ... peval = 0.1 ... ϵ-greedy strategy for exploration. ... LPMi(n(i) eval) = CMi(n(i) eval) - CMi(n(i) eval l). ... precision parameter ϵreach. |