Our goal is to model and experimentally assess trust evolution to predict future beliefs and behaviors of human-robot teams in dynamic environments. Research suggests that maintaining trust among team members in a human-robot team is vital for successful team performance. Research suggests that trust is a multi-dimensional and latent entity that relates to past experiences and future actions in a complex manner. Employing a human-robot collaborative task, we design an optimal assistance-seeking strategy for the robot using a POMDP framework. In the task, the human supervises an autonomous mobile manipulator collecting objects in an environment. The supervisor's task is to ensure that the robot safely executes its task. The robot can either choose to attempt to collect the object or seek human assistance. The human supervisor actively monitors the robot's activities, offering assistance upon request, and intervening if they perceive the robot may fail. In this setting, human trust is the hidden state, and the primary objective is to optimize team performance. We execute two sets of human-robot interaction experiments. The data from the first experiment are used to estimate POMDP parameters, which are used to compute an optimal assistance-seeking policy evaluated in the second experiment. The estimated POMDP parameters reveal that, for most participants, human intervention is more probable when trust is low, particularly in high-complexity tasks. Our estimates suggest that the robot's action of asking for assistance in high-complexity tasks can positively impact human trust. Our experimental results show that the proposed trust-aware policy is better than an optimal trust-agnostic policy. By comparing model estimates of human trust, obtained using only behavioral data, with the collected self-reported trust values, we show that model estimates are isomorphic to self-reported responses.

Offline-to-Online Reinforcement Learning has emerged as a powerful paradigm, leveraging offline data for initialization and online fine-tuning to enhance both sample efficiency and performance. However, most existing research has focused on single-agent settings, with limited exploration of the multi-agent extension, i.e., Offline-to-Online Multi-Agent Reinforcement Learning (O2O MARL). In O2O MARL, two critical challenges become more prominent as the number of agents increases: (i) the risk of unlearning pre-trained Q-values due to distributional shifts during the transition from offline-to-online phases, and (ii) the difficulty of efficient exploration in the large joint state-action space. To tackle these challenges, we propose a novel O2O MARL framework called Offline Value Function Memory with Sequential Exploration (OVMSE). First, we introduce the Offline Value Function Memory (OVM) mechanism to compute target Q-values, preserving knowledge gained during offline training, ensuring smoother transitions, and enabling efficient fine-tuning. Second, we propose a decentralized Sequential Exploration (SE) strategy tailored for O2O MARL, which effectively utilizes the pre-trained offline policy for exploration, thereby significantly reducing the joint state-action space to be explored. Extensive experiments on the StarCraft Multi-Agent Challenge (SMAC) demonstrate that OVMSE significantly outperforms existing baselines, achieving superior sample efficiency and overall performance.

The autonomous exploration of environments by multi-robot systems is a critical task with broad applications in rescue missions, exploration endeavors, and beyond. Current approaches often rely on either greedy frontier selection or end-to-end deep reinforcement learning (DRL) methods, yet these methods are frequently hampered by limitations such as short-sightedness, overlooking long-term implications, and convergence difficulties stemming from the intricate high-dimensional learning space. To address these challenges, this paper introduces an innovative integration strategy that combines the low-dimensional action space efficiency of frontier-based methods with the far-sightedness and optimality of DRL-based approaches. We propose a three-tiered planning framework that first identifies frontiers in free space, creating a sparse map representation that lightens data transmission burdens and reduces the DRL action space's dimensionality. Subsequently, we develop a multi-graph neural network (mGNN) that incorporates states of potential targets and robots, leveraging policy-based reinforcement learning to compute affinities, thereby superseding traditional heuristic utility values. Lastly, we implement local routing planning through subsequence search, which avoids exhaustive sequence traversal. Extensive validation across diverse scenarios and comprehensive simulation results demonstrate the effectiveness of our proposed method. Compared to baseline approaches, our framework achieves environmental exploration with fewer time steps and a notable reduction of over 30% in data transmission, showcasing its superiority in terms of efficiency and performance.

We study infinite-horizon average-reward reinforcement learning (RL) for Lipschitz MDPs and develop an algorithm ZoRL that discretizes the state-action space adaptively and zooms into promising regions of the state-action space. We show that its regret can be bounded as $\mathcal{\tilde{O}}\big(T^{1 - d_{\text{eff.}}^{-1}}\big)$, where $d_{\text{eff.}} = 2d_\mathcal{S} + d_z + 3$, $d_\mathcal{S}$ is the dimension of the state space, and $d_z$ is the zooming dimension. $d_z$ is a problem-dependent quantity, which allows us to conclude that if MDP is benign, then its regret will be small. We note that the existing notion of zooming dimension for average reward RL is defined in terms of policy coverings, and hence it can be huge when the policy class is rich even though the underlying MDP is simple, so that the regret upper bound is nearly $O(T)$. The zooming dimension proposed in the current work is bounded above by $d$, the dimension of the state-action space, and hence is truly adaptive, i.e., shows how to capture adaptivity gains for infinite-horizon average-reward RL. ZoRL outperforms other state-of-the-art algorithms in experiments; thereby demonstrating the gains arising due to adaptivity.

Autonomous agents require the capability to identify dynamic objects in their environment for safe planning and navigation. Incomplete and erroneous dynamic detections jeopardize the agent's ability to accomplish its task. Dynamic detection is a challenging problem due to the numerous sources of uncertainty inherent in the problem's inputs and the wide variety of applications, which often lead to use-case-tailored solutions. We propose a robust learning-free approach to segment moving objects in point cloud data. The foundation of the approach lies in modelling each voxel using a hidden Markov model (HMM), and probabilistically integrating beliefs into a map using an HMM filter. The proposed approach is tested on benchmark datasets and consistently performs better than or as well as state-of-the-art methods with strong generalized performance across sensor characteristics and environments. The approach is open-sourced at https://github.com/vb44/HMM-MOS.

Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning. How can we achieve cooperation among self-interested, independent learning agents? Promising recent work has shown that in certain tasks cooperation can be established between learning-aware agents who model the learning dynamics of each other. Here, we present the first unbiased, higher-derivative-free policy gradient algorithm for learning-aware reinforcement learning, which takes into account that other agents are themselves learning through trial and error based on multiple noisy trials. We then leverage efficient sequence models to condition behavior on long observation histories that contain traces of the learning dynamics of other agents. Training long-context policies with our algorithm leads to cooperative behavior and high returns on standard social dilemmas, including a challenging environment where temporally-extended action coordination is required. Finally, we derive from the iterated prisoner's dilemma a novel explanation for how and when cooperation arises among self-interested learning-aware agents.

Recent advances in multi-agent reinforcement learning (MARL) have created opportunities to solve complex real-world tasks. Cybersecurity is a notable application area, where defending networks against sophisticated adversaries remains a challenging task typically performed by teams of security operators. In this work, we explore novel MARL strategies for building autonomous cyber network defenses that address challenges such as large policy spaces, partial observability, and stealthy, deceptive adversarial strategies. To facilitate efficient and generalized learning, we propose a hierarchical Proximal Policy Optimization (PPO) architecture that decomposes the cyber defense task into specific sub-tasks like network investigation and host recovery. Our approach involves training sub-policies for each sub-task using PPO enhanced with domain expertise. These sub-policies are then leveraged by a master defense policy that coordinates their selection to solve complex network defense tasks. Furthermore, the sub-policies can be fine-tuned and transferred with minimal cost to defend against shifts in adversarial behavior or changes in network settings. We conduct extensive experiments using CybORG Cage 4, the state-of-the-art MARL environment for cyber defense. Comparisons with multiple baselines across different adversaries show that our hierarchical learning approach achieves top performance in terms of convergence speed, episodic return, and several interpretable metrics relevant to cybersecurity, including the fraction of clean machines on the network, precision, and false positives on recoveries.

Inventory control in modern supply chains has attracted significant attention due to the increasing number of disruptive shocks and the challenges posed by complex dynamics, uncertainties, and limited collaboration. Traditional methods, which often rely on static parameters, struggle to adapt to changing environments. This paper proposes a Multi-Agent Reinforcement Learning (MARL) framework with Graph Neural Networks (GNNs) for state representation to address these limitations. Our approach redefines the action space by parameterizing heuristic inventory control policies, making it adaptive as the parameters dynamically adjust based on system conditions. By leveraging the inherent graph structure of supply chains, our framework enables agents to learn the system's topology, and we employ a centralized learning, decentralized execution scheme that allows agents to learn collaboratively while overcoming information-sharing constraints. Additionally, we incorporate global mean pooling and regularization techniques to enhance performance. We test the capabilities of our proposed approach on four different supply chain configurations and conduct a sensitivity analysis. This work paves the way for utilizing MARL-GNN frameworks to improve inventory management in complex, decentralized supply chain environments.

A Bayesian coreset is a small, weighted subset of a data set that replaces the full data during inference to reduce computational cost. The state-of-the-art coreset construction algorithm, Coreset Markov chain Monte Carlo (Coreset MCMC), uses draws from an adaptive Markov chain targeting the coreset posterior to train the coreset weights via stochastic gradient optimization. However, the quality of the constructed coreset, and thus the quality of its posterior approximation, is sensitive to the stochastic optimization learning rate. In this work, we propose a learning-rate-free stochastic gradient optimization procedure, Hot-start Distance over Gradient (Hot DoG), Figure 1: Relative Coreset MCMC posterior approximation for training coreset weights in Coreset MCMC error (average squared coordinate-wise z-score) without user tuning effort. Empirical results using ADAM with different learning rates versus the demonstrate that Hot DoG provides higher proposed Hot DoG method (with fixed r = 0.001). Median quality posterior approximations than other values after 200,000 optimization iterations across learning-rate-free stochastic gradient methods, 10 trials are used for the relative comparison for a variety and performs competitively to optimallytuned of datasets, models, and coreset sizes.

Intelligent decisions can only be made based on the right information. When operating in the environment, an intelligent agent actively seeks the information that enables it to select the right actions and proceeds with the task only when it is confident enough. For example, when following a video recipe, a chef would look at the TV to obtain information about the next ingredient to grasp, and later look at a timer to decide when to turn off the stove. In contrast, current learning robots assume that the information needed for manipulation is readily available in their sensor signals (e.g., from a stationary camera looking at a tabletop manipulation setting) or rely on a given low-dimensional state representation predefined by a human (e.g., object pose) that also has to provide the means for the robot to perceive it. In this work, our goal is to endow robots with the capabilities to learn to perform information-seeking actions to find the information that enables manipulation, using as supervision the quality of the informed actions and switching between active perception and manipulation only based on the uncertainty about what manipulation action should come next. Performing actions to reveal information has been previously explored in the subfields of active and interactive perception. In active perception [1, 2, 3], an agent changes the parameters of its sensors (e.g., camera pose [4, 5, 6] or parameters [7, 8, 9]) to infer information such as object pose, shape, or material. Interactive perception [10] solutions go one step further and enable agents to change the state of the environment to create information-rich signals to perceive kinematics [11, 12], material [13], or other properties [14, 15, 16, 17].