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.
Hierarchical transformers are efficient meta- reinforcement learners.arXiv preprint arXiv:2402.06402,
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A survey provides a task-based formalization of meta-learning and meta-RL while chronicling algorithms that lead to DeepMind's Adaptive Agent.
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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.
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Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent
A survey provides a task-based formalization of meta-learning and meta-RL while chronicling algorithms that lead to DeepMind's Adaptive Agent.