Transferable Reinforcement Learning via Probabilistic Latent Embeddings and Dynamic Policy Adaptation for Sim-to-Real Deployment

In three linesRL framework for sim-to-real policy transfer via probabilistic latent embeddings and dynamic adaptation. Uses meta-RL and CMDPs to infer latent environment representation, with distributional RL formulation dynamically adjusting risk levels based on latent context estimation accuracy.

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