A rate-distortion based switching strategy for adaptive state-action abstractions in RL decomposes value error into Bellman residual and bisimulation metric terms to achieve near-optimal performance under lossy compression in tabular settings.
Scalable Methods for Computing State Simi- larity in Deterministic Markov Decision Processes, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp
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A VAE-based latent task representation enables automatic curriculum generation in CRL for non-Euclidean navigation tasks, outperforming interpolation and GAN-based methods in experiments.
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Adaptive state-action abstractions via rate-distortion
A rate-distortion based switching strategy for adaptive state-action abstractions in RL decomposes value error into Bellman residual and bisimulation metric terms to achieve near-optimal performance under lossy compression in tabular settings.
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Curriculum reinforcement learning with measurable task representation learning
A VAE-based latent task representation enables automatic curriculum generation in CRL for non-Euclidean navigation tasks, outperforming interpolation and GAN-based methods in experiments.