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The key challenge in making this connection is grounding the skills, so that each skill corresponds to a specific goal-conditioned policy. We start by recalling the definition of the discounted state occupancymeasure(Eq.3): p(st+=sg)=(1 γ) X On the second line, we havechanged the bounds of the summation to start at 0, and changed the terms inside the summation accordingly. On the third line, we applied linearity of expectation to movethesummation insidetheexpectation. Onthefourthline,weappliedlinearity ofexpectation again to move the term fort = 0 inside the expectation. Finally, we substituted the definition of rg(s,a)toobtainthedesiredresult. This result means that we are doing policy improvement with approximate Q-values.

To this end, Andrychowicz et al.[1] introduced Hindsight Experience Replay (HER), which can rapidly train goal-conditioned policies by retroactively imagining failed trajectories as successful ones.

NRNS outperforms RL-based formulations by a significant margin.

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As alluded to in Section 3, the formulation discussed in this paper is suitable for reversible environments. M. While the weight for entropy is automatically adjusted using dual A similar scheme to relabel the demonstration set can be followed. First, we describe the reward functions and the success metrics corresponding to each environment. The success metric is the same as the reward function. The success metric is the same as the reward function.

PIBT is a rule-based Multi-Agent Path Finding (MAPF) solver, widely used as a low-level planner or action sampler in many state-of-the-art approaches. Its primary advantage lies in its exceptional speed, enabling action selection for thousands of agents within milliseconds by considering only the immediate next timestep. However, this short-horizon design leads to poor performance in scenarios where agents have orientation and must perform time-consuming rotation actions. In this work, we present an enhanced version of PIBT that addresses this limitation by incorporating multi-action operations. We detail the modifications introduced to improve PIBT's performance while preserving its hallmark efficiency. Furthermore, we demonstrate how our method, when combined with graph-guidance technique and large neighborhood search optimization, achieves state-of-the-art performance in the online LMAPF-T setting.

In this article, we address the problem of collaborative task assignment, sequencing, and multi-agent pathfinding (TSPF), where a team of agents must visit a set of task locations without collisions while minimizing flowtime. TSPF incorporates agent-task compatibility constraints and ensures that all tasks are completed. We propose a Conflict-Based Search with Task Sequencing (CBS-TS), an optimal and complete algorithm that alternates between finding new task sequences and resolving conflicts in the paths of current sequences. CBS-TS uses a mixed-integer linear program (MILP) to optimize task sequencing and employs Conflict-Based Search (CBS) with Multi-Label A* (MLA*) for collision-free path planning within a search forest. By invoking MILP for the next-best sequence only when needed, CBS-TS efficiently limits the search space, enhancing computational efficiency while maintaining optimality. We compare the performance of our CBS-TS against Conflict-based Steiner Search (CBSS), a baseline method that, with minor modifications, can address the TSPF problem. Experimental results demonstrate that CBS-TS outperforms CBSS in most testing scenarios, achieving higher success rates and consistently optimal solutions, whereas CBSS achieves near-optimal solutions in some cases. The supplementary video is available at https://youtu.be/QT8BYgvefmU.

Left: A human partner is confined to a wheelchair and struggles to move past an obstacle.