SISL adds self-improving decoupled policies and return-based prioritization to skill-based meta-RL to achieve stable adaptation from noisy demonstrations on long-horizon tasks.
The low-level skill policyπ l,ϕ, skill encoderq ϕ, and skill priorp ϕ are parame- terized byϕand trained using the following loss function (modified from Eq
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Self-Improving Skill Learning for Robust Skill-based Meta-Reinforcement Learning
SISL adds self-improving decoupled policies and return-based prioritization to skill-based meta-RL to achieve stable adaptation from noisy demonstrations on long-horizon tasks.