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This work focuses on online reinforcement learning (RL) in the presence of en- vironmental constraints. Specifically, we consider applications involving robot agents exploring in an environment where obstacles and unsafe zones are present, and the agents must maximize cumulative rewards and at the same time meet the environmental constraints. To address this challenge, we formulate the prob- lem using the constrained Markov Decision Process (CMDP) and incorporate the environmental constraint costs into the policy updates in the proposed Aug- mented Proximal Policy Optimization (APPO) algorithm. At each state and for each possible action, we apply a Variational Auto-Encoder (VAE) [1] to obtain a probabilistic estimate of the discounted cumulative future environmental con- straint costs and integrate them as a regularization term to the reward function. This augmented reward function updates the action-value functions within the APPO algorithm, which is trained by an efficient optimization scheme. Ex- perimental results demonstrate that our methodology enables robot agents to navigate within the safety-constrained regions effectively. 

Snow Water Equivalent dataset contains information about snow water equivalent (SWE), temperature, precipitation, wind speed, relative humidity, and related climate variables across different locations and time periods. It includes daily observations and derived variables for hydrological and climate analysis. An additional column, source, specifies the origin of each data column. 

Abstract Plasmon polaritons, or plasmons, are coupled oscillations of electrons and electromagnetic fields that can confine the latter into deeply subwavelength scales, enabling novel polaritonic devices. While plasmons have been extensively studied in normal metals or semimetals, they remain largely unexplored in correlated materials. In this paper, we report infrared (IR) nano-imaging of thin flakes of CsV₃Sb₅, a prototypical layered Kagome metal. We observe propagating plasmon waves in real-space with wavelengths tunable by the flake thickness. From their frequency-momentum dispersion, we infer the out-of-plane dielectric function$${{{{{{\boldsymbol{\epsilon }}}}}}}_{{{{{{\boldsymbol{c}}}}}}}$$ ${ϵ}_c$that is generally difficult to obtain in conventional far-field optics, and elucidate signatures of electronic correlations when compared to density functional theory (DFT). We propose correlation effects might have switched the real part of$${{{{{{\boldsymbol{\epsilon }}}}}}}_{{{{{{\boldsymbol{c}}}}}}}$$ ${ϵ}_c$from negative to positive values over a wide range of middle-IR frequencies, transforming the surface plasmons into hyperbolic bulk plasmons, and have dramatically suppressed their dissipation.