CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
Authors: Cédric Colas, Pierre Fournier, Mohamed Chetouani, Olivier Sigaud, Pierre-Yves Oudeyer
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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. |