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.
A trajectoryτ i is added to Bi on if its returnG(τ i)exceeds the minimum return in the buffer,min τ ′∈Bion G(τ ′)
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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.