Proof of Theorem 4.7 We first prove a Lemma. Theorem A.2. (Theorem 1 in [36]) Let ϵ = max Theorem 4.7 In a two-player game, suppose that According to Theorem A.2, we have J ( π, µ) J ( π, α) E CQL [20] puts regularization on the learning of Q function to penalize out-of-distribution actions. The CSP algorithm is illustrated in Algorithm 1. The proxy model is trained adversarially against our agent, therefore, we set the proxy's reward function to be the negative of our agent's reward. We show experiment details of the Maze example in this section.

Beamforming-capable antenna arrays with many elements enable higher data rates in next generation 5G and 6G networks. In current practice, analog beamforming uses a codebook of pre-configured beams with each of them radiating towards a specific direction, and a beam management function continuously selects \textit{optimal} beams for moving user equipments (UEs). However, large codebooks and effects caused by reflections or blockages of beams make an optimal beam selection challenging. In contrast to previous work and standardization efforts that opt for supervised learning to train classifiers to predict the next best beam based on previously selected beams we formulate the problem as a partially observable Markov decision process (POMDP) and model the environment as the codebook itself. At each time step, we select a candidate beam conditioned on the belief state of the unobservable optimal beam and previously probed beams. This frames the beam selection problem as an online search procedure that locates the moving optimal beam. In contrast to previous work, our method handles new or unforeseen trajectories and changes in the physical environment, and outperforms previous work by orders of magnitude.

Nature is not a blocker to housing growth and the government risks missing both its housing and nature targets if it views it as one, a cross-party group of MPs has warned in a new report. The Planning and Infrastructure Bill overrides existing habitat protections, which the government has suggested is a barrier to its target to build 1.5 million houses by the end of this parliament. But in a report published on Sunday, the Environmental Audit Committee (EAC) found the measures outlined in the bill are not enough to allow the government to meet its goals. Using nature as a scapegoat means that the government will be less effective at tackling some of the genuine challenges facing the planning system, the report said. A Ministry of Housing spokesperson said it was fixing a failing system with landmark reforms, which would deliver a win-win for the economy and the environment.

Entity Matching (EM) is crucial for identifying equivalent data entities across different sources, a task that becomes increasingly challenging with the growth and heterogeneity of data. Blocking techniques, which reduce the computational complexity of EM, play a vital role in making this process scalable. Despite advancements in blocking methods, the issue of fairness; where blocking may inadvertently favor certain demographic groups; has been largely overlooked. This study extends traditional blocking metrics to incorporate fairness, providing a framework for assessing bias in blocking techniques. Through experimental analysis, we evaluate the effectiveness and fairness of various blocking methods, offering insights into their potential biases. Our findings highlight the importance of considering fairness in EM, particularly in the blocking phase, to ensure equitable outcomes in data integration tasks.

Automated testing of computer games is a challenging problem, especially when lengthy scenarios have to be tested. Automating such a scenario boils down to finding the right sequence of interactions given an abstract description of the scenario. Recent works have shown that an agent-based approach works well for the purpose, e.g. due to agents' reactivity, hence enabling a test agent to immediately react to game events and changing state. Many games nowadays are multi-player. This opens up an interesting possibility to deploy multiple cooperative test agents to test such a game, for example to speed up the execution of multiple testing tasks. This paper offers a cooperative multi-agent testing approach and a study of its performance based on a case study on a 3D game called Lab Recruits.

Blockers can take a tiny task and stretch it over days or weeks. Taking a moment at the beginning of a project to look for and prevent possible blockers can improve productivity. These examples of personal, team, and organizational levels show how gathering the right information and performing preflight checks can save hours of wasted time later. Two IT workers--Andrew and Bertie (not their real names)--were assigned the same task. While this task should have taken about an hour of hands-on keyboard work, it took Andrew four days. Andrew began the task one sunny Monday morning. Work went well until he hit a speed bump and needed to ask the requester (who we will call Roger) a question. Andrew tried to find him on the company chat system, only to learn Roger was out of the office. Andrew sent an email instead.

Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment. In this study, we integrated deep RL and BCI to improve beneficial human interventions in autonomous systems and the performance in decoding brain activities by considering environmental factors. Shared autonomy was allowed between the action command decoded from the electroencephalography (EEG) of the human agent and the action generated from the twin delayed DDPG (TD3) agent for a given environment. Our proposed copilot control scheme with a full blocker (Co-FB) significantly outperformed the individual EEG (EEG-NB) or TD3 control. The Co-FB model achieved a higher target approaching score, lower failure rate, and lower human workload than the EEG-NB model. The Co-FB control scheme had a higher invisible target score and level of allowed human intervention than the TD3 model. We also proposed a disparity d-index to evaluate the effect of contradicting agent decisions on the control accuracy and authority of the copilot model. We found a significant correlation between the control authority of the TD3 agent and the performance improvement of human EEG classification with respect to the d-index. We also observed that shifting control authority to the TD3 agent improved performance when BCI decoding was not optimal. These findings indicate that the copilot system can effectively handle complex environments and that BCI performance can be improved by considering environmental factors. Future work should employ continuous action space and different multi-agent approaches to evaluate copilot performance.

The goal of entity resolution is to identify records in multiple datasets that represent the same real-world entity. However, comparing all records across datasets can be computationally intensive, leading to long runtimes. To reduce these runtimes, entity resolution pipelines are constructed of two parts: a blocker that applies a computationally cheap method to select candidate record pairs, and a matcher that afterwards identifies matching pairs from this set using more expensive methods. This paper presents SC-Block, a blocking method that utilizes supervised contrastive learning for positioning records in the embedding space, and nearest neighbour search for candidate set building. We benchmark SC-Block against eight state-of-the-art blocking methods. In order to relate the training time of SC-Block to the reduction of the overall runtime of the entity resolution pipeline, we combine SC-Block with four matching methods into complete pipelines. For measuring the overall runtime, we determine candidate sets with 99.5% pair completeness and pass them to the matcher. The results show that SC-Block is able to create smaller candidate sets and pipelines with SC-Block execute 1.5 to 2 times faster compared to pipelines with other blockers, without sacrificing F1 score. Blockers are often evaluated using relatively small datasets which might lead to runtime effects resulting from a large vocabulary size being overlooked. In order to measure runtimes in a more challenging setting, we introduce a new benchmark dataset that requires large numbers of product offers to be blocked. On this large-scale benchmark dataset, pipelines utilizing SC-Block and the best-performing matcher execute 8 times faster than pipelines utilizing another blocker with the same matcher reducing the runtime from 2.5 hours to 18 minutes, clearly compensating for the 5 minutes required for training SC-Block.

Task and motion planning problems in robotics typically combine symbolic planning over discrete task variables with motion optimization over continuous state and action variables, resulting in trajectories that satisfy the logical constraints imposed on the task variables. Symbolic planning can scale exponentially with the number of task variables, so recent works such as PDDLStream have focused on optimistic planning with an incrementally growing set of objects and facts until a feasible trajectory is found. However, this set is exhaustively and uniformly expanded in a breadth-first manner, regardless of the geometric structure of the problem at hand, which makes long-horizon reasoning with large numbers of objects prohibitively time-consuming. To address this issue, we propose a geometrically informed symbolic planner that expands the set of objects and facts in a best-first manner, prioritized by a Graph Neural Network based score that is learned from prior search computations. We evaluate our approach on a diverse set of problems and demonstrate an improved ability to plan in large or difficult scenarios. We also apply our algorithm on a 7DOF robotic arm in several block-stacking manipulation tasks.