TAVT improves OOD task generalization in meta-RL by preserving task characteristics in virtual tasks via metric learning and using state regularization.
Mamba: an effective world model approach for meta-reinforcement learning
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The work introduces behavior-invariant latent task representations via information-theoretic learning in a Transformer world model plus conservative penalties on imagined rollouts to improve generalization in offline meta-RL.
citing papers explorer
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Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks
TAVT improves OOD task generalization in meta-RL by preserving task characteristics in virtual tasks via metric learning and using state regularization.
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Behavior-Invariant Task Representation Learning with Transformer-based World Models for Offline Meta-Reinforcement Learning
The work introduces behavior-invariant latent task representations via information-theoretic learning in a Transformer world model plus conservative penalties on imagined rollouts to improve generalization in offline meta-RL.