Applying Jensen's inequality, we can see that, for s arg min Combining these inequalities, along with the fact γ < 1, we conclude that g(s) 0 for all s S. Again applying Jensen's inequality to (9), for s arg max Hence, as γ < 1, we conclude that g(s) 0 for all s S. Combining these results, we conclude that g 0, that is, V Proof of Theorem 2. From Theorem 1, if we can determine the value function for one of our agents, then the reward is uniquely identified. Given we know both agents' policies (π, π) and our agents are optimizing their respective MDPs, for every a A, s S, we know the value of λ log π(a|s) π(a|s) = γ T (s Therefore, the space of solutions to (10) is either empty (in which case no consistent reward exists), or determines v up to the addition of a constant. Given v is determined up to a constant we can use Theorem 1 to determine f, again up to the addition of a constant. Let R N be a set of natural numbers, with the property that R is closed under addition (if a, b R then a + b R). Suppose R has greatest common divisor 1 (i.e.

From game-changing text diffusion models and cutting-edge AR glasses to AI videos with sound and virtual clothing try-ons, there was plenty of amazing tech to see during Google's I/O keynote on Tuesday. The closest we got to a smart home shout-out was when a Google exec said that Gemini--the star of the show--is "coming to your watch, your car dashboard, even your TV." As Google puts its Google TV Streamer under the umbrella of smart home, we'll count that as a fleeting reference. Officially, Google has promised that Gemini is coming to Nest devices. Gemini on Nest speakers has been available on a public-preview basis for months now, and back in March, Google confirmed that a "new experience powered by Gemini" is coming to smart speakers and displays.

Multi-agent control is a central theme in the Cyber-Physical Systems (CPS). However, current control methods either receive non-Markovian states due to insufficient sensing and decentralized design, or suffer from poor convergence. This paper presents the Delayed Propagation Transformer (DePT), a new transformerbased model that specializes in the global modeling of CPS while taking into account the immutable constraints from the physical world. DePT induces a cone-shaped spatial-temporal attention prior, which injects the information propagation and aggregation principles and enables a global view. With physical constraint inductive bias baked into its design, our DePT is ready to plug and play for a broad class of multi-agent systems. The experimental results on one of the most challenging CPS - network-scale traffic signal control system in the open world - show that our model outperformed the state-of-the-art expert methods on synthetic and real-world datasets.

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We study two problems related to recovering causal graphs from interventional data: (i) verification, where the task is to check if a purported causal graph is correct, and (ii) search, where the task is to recover the correct causal graph. For both, we wish to minimize the number of interventions performed. For the first problem, we give a characterization of a minimal sized set of atomic interventions that is necessary and sufficient to check the correctness of a claimed causal graph. Our characterization uses the notion of covered edges, which enables us to obtain simple proofs and also easily reason about earlier known results. We also generalize our results to the settings of bounded size interventions and node-dependent interventional costs.

SAVE 30: As of May 21, the Fire TV Stick 4K Max is on sale for only 29.99 at Amazon with the code MAX4KNEW. Amazon's Fire TV Stick 4K Max can turn your existing TV into artwork for that bougie on a budget aesthetic. As of May 21, the already affordable Fire TV Stick 4K Max is on sale for a very low 29.99 at Amazon when you enter the code MAX4KNEW. The first and only Fire TV Stick with Ambient Experience, the 4K Max lets you choose from over 2,000 works of fine art or use AI art to design your own masterpiece by asking Alexa to generate an original image and choosing a painting style. The only limit is your own imagination. You can also customize your screen with Alexa widgets like calendar, to-dos, weather, and more, and control your compatible smart home devices from your TV.

We thank all reviewers for their careful reviews and many positive comments. We feel that the typos and minor issues are easily addressable and will be corrected. We will incorporate this analysis into a revision of the paper. We thank R1 for bringing this highly related work to our attention. That work focuses on environments for which mice have previously developed spatial maps.

In real-world decision making tasks, it is critical for data-driven reinforcement learning methods to be both stable and sample efficient. On-policy methods typically generate reliable policy improvement throughout training, while off-policy methods make more efficient use of data through sample reuse. In this work, we combine the theoretically supported stability benefits of on-policy algorithms with the sample efficiency of off-policy algorithms. We develop policy improvement guarantees that are suitable for the off-policy setting, and connect these bounds to the clipping mechanism used in Proximal Policy Optimization. This motivates an off-policy version of the popular algorithm that we call Generalized Proximal Policy Optimization with Sample Reuse. We demonstrate both theoretically and empirically that our algorithm delivers improved performance by effectively balancing the competing goals of stability and sample efficiency.

We conduct further ablation studies for our approach in this supplementary material, including comparison with test-time optimization and sensitivity analysis of the refinement stage. Additional qualitative results are also provided. We compare our coarse-to-fine approach with the testtime optimization approach. As has been done in our coarse-to-fine pipeline, we also use the output from our coarse estimation stage as an initialization. Instead of apply the mesh refinement GCN, we further optimize the SMAL parameters based on the keypoints and silhouettes for 10, 50, 100, 200 iterations, respectively.

Most existing animal pose and shape estimation approaches reconstruct animal meshes with a parametric SMAL model. This is because the low-dimensional pose and shape parameters of the SMAL model makes it easier for deep networks to learn the high-dimensional animal meshes. However, the SMAL model is learned from scans of toy animals with limited pose and shape variations, and thus may not be able to represent highly varying real animals well. This may result in poor fittings of the estimated meshes to the 2D evidences, e.g.