Designing Reinforcement Learning (RL) solutions for real-life problems remains a significant challenge. A major area of concern is safety. "Shielding" is a popular technique to enforce safety in RL by turning user-defined safety specifications into safe agent behavior. However, these methods either suffer from extreme learning delays, demand extensive human effort in designing models and safe domains in the problem, or require pre-computation. In this paper, we propose a new permissibility-based framework to deal with safety and shield construction. Permissibility was originally designed for eliminating (non-permissible) actions that will not lead to an optimal solution to improve RL training efficiency. This paper shows that safety can be naturally incorporated into this framework, i.e. extending permissibility to include safety, and thereby we can achieve both safety and improved efficiency. Experimental evaluation using three standard RL applications shows the effectiveness of the approach.

Generative agents, which implement behaviors using a large language model (LLM) to interpret and evaluate an environment, has demonstrated the capacity to solve complex tasks across many social and technological domains. However, when these agents interact with other agents and humans in presence of social structures such as existing norms, fostering cooperation between them emerges as a fundamental challenge. In this paper, we develop the framework of a Normative Module: an architecture for generative agents designed to enhance cooperation by enabling agents to recognize and adapt to the normative infrastructure of a given environment, in the form of institutions that define acceptable behaviors within a group of agents. We focus on the equilibrium selection aspect of the cooperation problem and inform our agent design based on the existence of classification institutions that implement correlated equilibrium to provide effective resolution of the equilibrium selection problem. Specifically, the normative module enables agents to learn through peer interactions which of multiple candidate institutions in the environment, does a group treat as authoritative. By enabling normative competence in this sense, agents gain ability to coordinate their sanctioning behaviour; coordinated sanctioning behaviour in turn shapes primary behaviour within a social environment, leading to higher average welfare We design a new environment that supports institutions and evaluate the proposed framework based on two key criteria derived from agent interactions with peers and institutions: (i) the agent's ability to disregard non-authoritative institutions and (ii) the agent's ability to identify authoritative institutions among several options. Crucially, we show that these capabilities allow the agent to achieve more stable cooperative outcomes compared to baseline agents without the normative module, paving the way for future research in a new avenue of designing environments and agents that account for normative infrastructure.

We explore a Federated Reinforcement Learning (FRL) problem where $N$ agents collaboratively learn a common policy without sharing their trajectory data. To date, existing FRL work has primarily focused on agents operating in the same or ``similar" environments. In contrast, our problem setup allows for arbitrarily large levels of environment heterogeneity. To obtain the optimal policy which maximizes the average performance across all potentially completely different environments, we propose two algorithms: FedSVRPG-M and FedHAPG-M. In contrast to existing results, we demonstrate that both FedSVRPG-M and FedHAPG-M, both of which leverage momentum mechanisms, can exactly converge to a stationary point of the average performance function, regardless of the magnitude of environment heterogeneity. Furthermore, by incorporating the benefits of variance-reduction techniques or Hessian approximation, both algorithms achieve state-of-the-art convergence results, characterized by a sample complexity of $\mathcal{O}\left(\epsilon^{-\frac{3}{2}}/N\right)$. Notably, our algorithms enjoy linear convergence speedups with respect to the number of agents, highlighting the benefit of collaboration among agents in finding a common policy.

Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks, while the effective design of multiple agents for a particular application remains an art. It is thus intriguing to answer a critical question: Given a task, how can we build a team of LLM agents to solve it effectively? Our new adaptive team-building paradigm offers a flexible solution, realized through a novel agent design named Captain Agent. It dynamically forms and manages teams for each step of a task-solving process, utilizing nested group conversations and reflection to ensure diverse expertise and prevent stereotypical outputs. It allows for a flexible yet structured approach to problem-solving and can help reduce redundancy and enhance output diversity. A comprehensive evaluation across six real-world scenarios demonstrates that Captain Agent significantly outperforms existing multi-agent methods with 21.94% improvement in average accuracy, providing outstanding performance without requiring task-specific prompt engineering.

Min-max problems are important in multi-agent sequential decision-making because they improve the performance of the worst-performing agent in the network. However, solving the multi-agent min-max problem is challenging. We propose a modular, distributed, online planning-based algorithm that is able to approximate the solution of the min-max objective in networked Markov games, assuming that the agents communicate within a network topology and the transition and reward functions are neighborhood-dependent. This set-up is encountered in the multi-robot setting. Our method consists of two phases at every planning step. In the first phase, each agent obtains sample returns based on its local reward function, by performing online planning. Using the samples from online planning, each agent constructs a concave approximation of its underlying local return as a function of only the action of its neighborhood at the next planning step. In the second phase, the agents deploy a distributed optimization framework that converges to the optimal immediate next action for each agent, based on the function approximations of the first phase. We demonstrate our algorithm's performance through formation control simulations.

Decision making demands intricate interplay between perception, memory, and reasoning to discern optimal policies. Conventional approaches to decision making face challenges related to low sample efficiency and poor generalization. In contrast, foundation models in language and vision have showcased rapid adaptation to diverse new tasks. Therefore, we advocate for the construction of foundation agents as a transformative shift in the learning paradigm of agents. This proposal is underpinned by the formulation of foundation agents with their fundamental characteristics and challenges motivated by the success of large language models (LLMs). Moreover, we specify the roadmap of foundation agents from large interactive data collection or generation, to self-supervised pretraining and adaptation, and knowledge and value alignment with LLMs. Lastly, we pinpoint critical research questions derived from the formulation and delineate trends for foundation agents supported by real-world use cases, addressing both technical and theoretical aspects to propel the field towards a more comprehensive and impactful future.

We consider a decentralized optimization problem for networks affected by communication delays. Examples of such networks include collaborative machine learning, sensor networks, and multi-agent systems. To mimic communication delays, we add virtual non-computing nodes to the network, resulting in directed graphs. This motivates investigating decentralized optimization solutions on directed graphs. Existing solutions assume nodes know their out-degrees, resulting in limited applicability. To overcome this limitation, we introduce a novel gossip-based algorithm, called DT-GO, that does not need to know the out-degrees. The algorithm is applicable in general directed networks, for example networks with delays or limited acknowledgment capabilities. We derive convergence rates for both convex and non-convex objectives, showing that our algorithm achieves the same complexity order as centralized Stochastic Gradient Descent. In other words, the effects of the graph topology and delays are confined to higher-order terms. Additionally, we extend our analysis to accommodate time-varying network topologies. Numerical simulations are provided to support our theoretical findings.

Federated reinforcement learning (FRL) is a distributed learning framework that combines the principles of reinforcement learning (RL) [1] and federated learning (FL) [2]. Focusing on sequential decision-making, FRL aims to learn an optimal policy through parallel explorations by multiple agents under the coordination of a central server. Often modeled as a Markov decision process (MDP), multiple agents independently interact with an initially unknown environment and collaboratively train their decision-making models with limited information exchange between the agents. This approach accelerates the learning process with low communication costs. Some model-based algorithms (e.g., [3]) and policy-based algorithms (e.g., [4]) have shown speedup with respect to the number of agents in terms of learning regret or convergence rate. Recent progress has been made in FRL algorithms based on model-free value-based approaches, which directly learn the value functions and the optimal policy without estimating the underlying model (e.g., [5]). However, most existing model-free federated algorithms do not actively update the exploration policies for local agents and fail to provide low regret. A comprehensive literature review is provided in Appendix A.

Explainable AI Planning (XAIP) aims to develop AI agents that can effectively explain their decisions and actions to human users, fostering trust and facilitating human-AI collaboration. A key challenge in XAIP is model reconciliation, which seeks to align the mental models of AI agents and humans. While existing approaches often assume a known and deterministic human model, this simplification may not capture the complexities and uncertainties of real-world interactions. In this paper, we propose a novel framework that enables AI agents to learn and update a probabilistic human model through argumentation-based dialogues. Our approach incorporates trust-based and certainty-based update mechanisms, allowing the agent to refine its understanding of the human's mental state based on the human's expressed trust in the agent's arguments and certainty in their own arguments. We employ a probability weighting function inspired by prospect theory to capture the relationship between trust and perceived probability, and use a Bayesian approach to update the agent's probability distribution over possible human models. We conduct a human-subject study to empirically evaluate the effectiveness of our approach in an argumentation scenario, demonstrating its ability to capture the dynamics of human belief formation and adaptation.

This paper presents a novel design of a multi-agent system framework that applies a large language model (LLM) to automate the parametrization of process simulations in digital twins. We propose a multi-agent framework that includes four types of agents: observation, reasoning, decision and summarization. By enabling dynamic interaction between LLM agents and simulation model, the developed system can automatically explore the parametrization of the simulation and use heuristic reasoning to determine a set of parameters to control the simulation to achieve an objective. The proposed approach enhances the simulation model by infusing it with heuristics from LLM and enables autonomous search for feasible parametrization to solve a user task. Furthermore, the system has the potential to increase user-friendliness and reduce the cognitive load on human users by assisting in complex decision-making processes. The effectiveness and functionality of the system are demonstrated through a case study, and the visualized demos are available at a GitHub Repository: https://github.com/YuchenXia/LLMDrivenSimulation