What is a useful skill hierarchy for an autonomous agent?

Reinforcement learning has shown promising results in learning neural network policies for complicated control tasks. However, the lack of formal guarantees about the behavior of such policies remains an impediment to their deployment.

In this paper, we improve AlphaStar's league training

The contents of this supplementary material are organized as follows: Section A provides additional experimental results, including more results with three modalities, performance under dynamic weathers, performance under several challenging or extreme environmental conditions ( e.g., increased number of vehicles and dazzling sunlight), results on DeepMind Control Suit, and ablation study of auxiliary losses and the design of re-fusion. Section B provides further discussions related to our approach. This includes a comparison between value-level dynamic fusion and feature-level dynamic fusion supported by empirical results, the advantages of hierarchical bi-level fusion over uni-level fusion, and the relationship and differences between our approach and the value decomposition techniques in multi-agent RL. Section C describes the details of the experimental setup, including network architectures, hyper-parameters, and hardware details. Section D states the potential negative societal impacts of our work.

More examples include but are not limited to robotics (Mahmood et al.,

Multi-Agent Reinforcement Learning (GoMARL), which learns automatic grouping without domain knowledge for efficient cooperation.

A major challenge in the analysis of multi-agent systems is the restriction on joint policies of agents.

The growing use of smartphones and IoT devices necessitates efficient time-series analysis on resource-constrained hardware, which is critical for sensing applications such as human activity recognition and air quality prediction. Recent efforts in hardware-aware neural architecture search (NAS) automate architecture discovery for specific platforms; however, none focus on general time-series analysis with edge deployment. Leveraging the problem-solving and reasoning capabilities of large language models (LLM), we propose MONAQ, a novel framework that reformulates NAS into Multi-Objective Neural Architecture Querying tasks. MONAQ is equipped with multimodal query generation for processing multimodal time-series inputs and hardware constraints, alongside an LLM agent-based multi-objective search to achieve deployment-ready models via code generation. By integrating numerical data, time-series images, and textual descriptions, MONAQ improves an LLM's understanding of time-series data. Experiments on fifteen datasets demonstrate that MONAQ-discovered models outperform both handcrafted models and NAS baselines while being more efficient.