Vector-Valued Distributional Reinforcement Learning Policy Evaluation: A Hilbert Space Embedding Approach

arXiv:2601.18952 · cs.LG, stat.ME, stat.ML · Submitted 2026-01-26 · Read on arXiv

cs.LG, stat.ME, stat.ML

Submitted: 2026-01-26

Updated: 2026-09-19

Code: https://github.com/mehrdadmhmdi/ke-drl

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Embeddings — 36× in this paper · explained in 2 episodes
• Benchmark — 26× in this paper · explained in 2 episodes
• Regularization — 22× in this paper · explained in 3 episodes
• Consistency — 13× in this paper · explained in 3 episodes
• Reinforcement Learning — 11× in this paper · explained in 4 episodes
• Hilbert Space — 10× in this paper · explained in 2 episodes
• Wasserstein Distance — 9× in this paper · explained in 4 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Inverse Problem — 3× in this paper · explained in 2 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Exchangeability — 2× in this paper · explained in 2 episodes
• Optimal Transport — 2× in this paper · explained in 5 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Causal Discovery — 1× in this paper · explained in 2 episodes
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 24 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

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