Inthissection compare compare learned theprior environment 6. Experimental Tostudy proposed [54], where froman learning.

OfflineRLisdeemed to be promising [16, 14] as online learning requires the agent to continuously interact with the environment, which howevermaybecostly,time-consuming, orevendangerous.

A challenge in mobile sensing is to overcome the location uncertainty of its sensors.

Our goal is to bridge the gap between deep reinforcement learning research and industrial manufacturing by creating simulation environments that model real-world factories.

Infact, modelingmulti-modal 3 k means Continuous action dataset (|A| x a)Clustering into k bins Action offset (1 x a)Continuous action (1 x a)Categorical action bin (1 x k)Continuous action (1 x a)k means encoderk means decoderA.

Thelimited datainbatchRLproduces inherent uncertainty in value estimates of states/actions that were insufficiently represented in the training data.

Specifically, there now exists a tight relaxation for verifying therobustness ofaneural networkto` input perturbations, aswell asefficient primal and dual solvers for the relaxation. Buoyed by this success, we consider the problem of developing similar techniques for verifying robustness to input perturbations within the probability simplex. We prove a somewhat surprising result that,inthiscase, notonlycanonedesign atightrelaxation thatovercomes the convexbarrier,butthe size ofthe relaxation remains linear inthe number of neurons, thereby leading tosimpler and more efficient algorithms.

Reinforcement learning (RL) is typically concerned with estimating stationary policies orsingle-step models, leveraging theMarkovproperty tofactorize problems in time. However, we can also view RL as a generic sequence modeling problem, with the goal being to produce a sequence of actions that leads to a sequence ofhighrewards.

We establish an optimal sample complexity of $O(ε^{-2})$ for obtaining an $ε$-optimal global policy using a single-timescale actor-critic (AC) algorithm in infinite-horizon discounted Markov decision processes (MDPs) with finite state-action spaces, improving upon the prior state of the art of $O(ε^{-3})$. Our approach applies STORM (STOchastic Recursive Momentum) to reduce variance in the critic updates. However, because samples are drawn from a nonstationary occupancy measure induced by the evolving policy, variance reduction via STORM alone is insufficient. To address this challenge, we maintain a buffer of small fraction of recent samples and uniformly sample from it for each critic update. Importantly, these mechanisms are compatible with existing deep learning architectures and require only minor modifications, without compromising practical applicability.