Abstract:This paper presents a comprehensive overview of autotelic Reinforcement Learning (RL), emphasizing the role of intrinsic motivations in the open-ended formation of skill repertoires. We delineate the distinctions between knowledge-based and competence-based intrinsic motivations, illustrating how these concepts inform the development of autonomous agents capable of generating and pursuing self-defined goals. The typology of Intrinsically Motivated Goal Exploration Processes (IMGEPs) is explored, with a focus on the implications for multi-goal RL and developmental robotics. The autotelic learning problem is framed within a reward-free Markov Decision Process (MDP), WHERE agents must autonomously represent, generate, and master their own goals. We address the unique challenges in evaluating such agents, proposing various metrics for measuring exploration, generalization, and robustness in complex environments. This work aims to advance the understanding of autotelic RL agents and their potential for enhancing skill acquisition in a diverse and dynamic setting.
| Comments: | 12 pages, 12 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2502.04418 [cs.LG] |
| (or arXiv:2502.04418v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2502.04418 arXiv-issued DOI via DataCite |
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| Journal reference: | International Journal of Computer Trends and Technology, vol. 73, 2025 |
| Related DOI: | https://doi.org/10.14445/22312803/IJCTT-V73I1P104
DOI(s) linking to related resources |
Submission history
From: Prakhar Srivastava [view email]
[v1]
Thu, 6 Feb 2025 14:37:46 UTC (1,234 KB)