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Accelerating Reinforcement Learning with Learned Skill Priors
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Intelligent agents rely heavily on prior experience when learning a new task, yet most modern reinforcement learning (RL) approaches learn every task from scratch. One approach for leveraging prior knowledge is to transfer skills learned on prior tasks to the new task. However, as the amount of prior experience increases, the number of transferable skills grows too, making it challenging to explore the full set of available skills during downstream learning. Yet, intuitively, not all skills should be explored with equal probability; for example information about the current state can hint which skills are promising to explore. In this work, we propose to implement this intuition by learning a prior over skills. We propose a deep latent variable model that jointly learns an embedding space of skills and the skill prior from offline agent experience. We then extend common maximum-entropy RL approaches to use skill priors to guide downstream learning. We validate our approach, SPiRL (Skill-Prior RL), on complex navigation and robotic manipulation tasks and show that learned skill priors are essential for effective skill transfer from rich datasets. Videos and code are available at https://clvrai.com/spirl.
Forward citations
Cited by 7 Pith papers
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Decision Transformer: Reinforcement Learning via Sequence Modeling
Decision Transformer casts RL as autoregressive sequence modeling conditioned on desired returns, past states and actions, matching or exceeding offline RL baselines on Atari, Gym and Key-to-Door tasks.
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Atomic-Probe Governance for Skill Updates in Compositional Robot Policies
A cross-version swap protocol reveals dominant skills that swing composition success by up to 50 percentage points, and an atomic probe with selective revalidation governs updates at lower cost than always re-testing ...
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Governed Capability Evolution: Lifecycle-Time Compatibility Checking and Rollback for AI-Component-Based Systems, with Embodied Agents as Case Study
A governed capability evolution framework with interface, policy, behavioral, and recovery checks reduces unsafe activations to zero in embodied agent upgrades while preserving task success rates.
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Atomic-Probe Governance for Skill Updates in Compositional Robot Policies
Empirical study on robosuite tasks reveals a dominant-skill effect in compositions and shows that an atomic probe approximates full revalidation for skill updates at much lower cost.
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Governed Capability Evolution: Lifecycle-Time Compatibility Checking and Rollback for AI-Component-Based Systems, with Embodied Agents as Case Study
A governed capability evolution framework for embodied agents uses four compatibility checks and a staged pipeline to achieve zero unsafe activations during upgrades while retaining comparable task success rates.
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What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
A comprehensive benchmark study of offline imitation learning methods on multi-stage robot manipulation tasks identifies key sensitivities to algorithm design, data quality, and stopping criteria while releasing all d...
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LAMP: Latent Motion Prior-Guided Real-World Learning for Dexterous Hand Manipulation
A history-conditioned latent motion prior enables real-world dexterous hand policies to improve from 56% to 99% success via residual reinforcement learning without breaking contact.
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