Autotelic Agents With Intrinsically Motivated Goal-conditioned Reinforcement Learning: A Short Survey

Abstract

Building autonomous machines that can explore open-ended environments, discover possible interactions and build repertoires of skills is a general objective of artificial intelligence. Developmental approaches argue that this can only be achieved by \(autotelic\) \(agents\): intrinsically motivated learning agents that can learn to represent, generate, select and solve their own problems. In recent years, the convergence of developmental approaches with deep reinforcement learning (RL) methods has been leading to the emergence of a new field: \(developmental\) \(reinforcement\) \(learning\). Developmental RL is concerned with the use of deep RL algorithms to tackle a developmental problem -- the \(intrinsically\) \(motivated\) \(acquisition\) \(of\) \(open\)-\(ended\) \(repertoires\) \(of\) \(skills\). The self-generation of goals requires the learning of compact goal encodings as well as their associated goal-achievement functions. This raises new challenges compared to standard RL al

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keycolas2020autotelic

Related papers

• Autotelic Reinforcement Learning: Exploring Intrinsic Motivations For Skill Acquisition In Open-ended Environments (2025)5.24
• Autotelic Reinforcement Learning In Multi-agent Environments (2022)0.00
• Learning With Amigo: Adversarially Motivated Intrinsic Goals (2020)0.00
• Intrinsic Motivation In Model-based Reinforcement Learning: A Brief Review (2023)0.00
• Generating Automatic Curricula Via Self-supervised Active Domain Randomization (2020)0.00
• A Survey On Intrinsic Motivation In Reinforcement Learning (2019)0.00
• An Information-theoretic Perspective On Intrinsic Motivation In Reinforcement Learning: A Survey (2022)11.93
• Deep Reinforcement Learning For Multi-agent Interaction (2022)9.23