Continual Learning Of Control Primitives: Skill Discovery Via Reset-games

Abstract

Reinforcement learning has the potential to automate the acquisition of behavior in complex settings, but in order for it to be successfully deployed, a number of practical challenges must be addressed. First, in real world settings, when an agent attempts a task and fails, the environment must somehow "reset" so that the agent can attempt the task again. While easy in simulation, this could require considerable human effort in the real world, especially if the number of trials is very large. Second, real world learning often involves complex, temporally extended behavior that is often difficult to acquire with random exploration. While these two problems may at first appear unrelated, in this work, we show how a single method can allow an agent to acquire skills with minimal supervision while removing the need for resets. We do this by exploiting the insight that the need to "reset" an agent to a broad set of initial states for a learning task provides a natural setting to learn a div

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyxu2020continual

Related papers

• Leave No Trace: Learning To Reset For Safe And Autonomous Reinforcement Learning (2017)0.00
• Focused Skill Discovery: Learning To Control Specific State Variables While Minimizing Side Effects (2025)0.00
• Neuroevolution Is A Competitive Alternative To Reinforcement Learning For Skill Discovery (2022)0.00
• Skills: Adaptive Skill Sequencing For Efficient Temporally-extended Exploration (2022)0.00
• Learning Without Time-based Embodiment Resets In Soft-actor Critic (2025)0.00
• Stochastic Resetting Accelerates Policy Convergence In Reinforcement Learning (2026)0.00
• Learning More Skills Through Optimistic Exploration (2021)0.00
• Generating Automatic Curricula Via Self-supervised Active Domain Randomization (2020)0.00