Autonomous Discovery Of The Goal Space To Learn A Parameterized Skill

Abstract

A parameterized skill is a mapping from multiple goals/task parameters to the policy parameters to accomplish them. Existing works in the literature show how a parameterized skill can be learned given a task space that defines all the possible achievable goals. In this work, we focus on tasks defined in terms of final states (goals), and we face on the challenge where the agent aims to autonomously acquire a parameterized skill to manipulate an initially unknown environment. In this case, the task space is not known a priori and the agent has to autonomously discover it. The agent may posit as a task space its whole sensory space (i.e. the space of all possible sensor readings) as the achievable goals will certainly be a subset of this space. However, the space of achievable goals may be a very tiny subspace in relation to the whole sensory space, thus directly using the sensor space as task space exposes the agent to the curse of dimensionality and makes existing autonomous skill acqu

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keycartoni2018autonomous

Related papers

• Focused Skill Discovery: Learning To Control Specific State Variables While Minimizing Side Effects (2025)0.00
• Skild: Unsupervised Skill Discovery Guided By Factor Interactions (2024)0.00
• Autotelic Reinforcement Learning In Multi-agent Environments (2022)0.00
• Learning More Skills Through Optimistic Exploration (2021)0.00
• Skills: Adaptive Skill Sequencing For Efficient Temporally-extended Exploration (2022)0.00
• Dream To Explore: Adaptive Simulations For Autonomous Systems (2021)0.00
• SKILL0: In-context Agentic Reinforcement Learning For Skill Internalization (2026)0.00
• Growing Action Spaces (2019)0.00