핵심 개념
Task inference sequence models are beneficial in meta-RL, even without task inference objectives.
초록
Meta-RL aims to create agents for rapid learning in novel tasks.
Black box methods train sequence models end-to-end, while task inference methods infer a posterior over tasks.
Recent evidence questions the necessity of task inference objectives.
SplAgger combines permutation variant and invariant components, outperforming baselines.
Experiments show SplAgger's advantage in continuous control and memory environments.
Different sequence models like RNN, PEARL, AMRL, and CNP are compared.
In-context learning is crucial for RL progress.
The paper proposes SplAgger, a model combining the best of both worlds.
통계
"A core ambition of reinforcement learning (RL) is the creation of agents capable of rapid learning in novel tasks."
"Recent evidence suggests that task inference objectives are unnecessary in practice."
"SplAgger uses both permutation variant and invariant components to achieve the best of both worlds."
인용구
"We present strong evidence that task inference sequence models are still beneficial."
"SplAgger outperforms all baselines on continuous control and memory environments."