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新论文 · v1arXiv:2611.14846

Open Challenges and Opportunities in Human-Agent Systems

Reuth Mirsky, Sarath Sreedharan, Nitay Alon, Stefan Sarkadi, Munindar P. Singh, Liz Sonenberg, Peter Stone, Silvia Tulli 等 10 位作者

原文摘要

Agentic AI systems increasingly undertake extended tasks and act on people's behalf, making human-agent interaction central to their effectiveness. Successful interaction requires understanding human perspectives, coordinating complementary capabilities, and maintaining productive relationships within socio-technical environments. This paper examines how foundations developed by the Autonomous Agents and Multi-Agent Systems (AAMAS) community inform these demands. We consider interactions ranging from individual human-agent partnerships to teams and larger groups, where participants differ in their roles, decision authority, capabilities, and objectives. We organize computational approaches around the connected challenges of understanding others and deciding how to interact with them. Theory of Mind, belief-desire-intention architectures, goal and plan recognition, inverse planning, and recursive reasoning provide mechanisms for representing and inferring partially observable mental states. Observer-aware planning, model reconciliation, and decision-theoretic communication connect these inferences to action, explanation, and information exchange. Together with shared autonomy, argumentation, and cooperative inverse reinforcement learning, these approaches offer foundations for agentic systems that account for evolving human knowledge, intentions, and expectations. We argue that progress requires treating human interaction as an ongoing decision problem shaped by uncertainty, interdependence, and potentially conflicting objectives.

cs.AI · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.16906

The Trajectory Alignment Coefficient in Two Acts: From Reward Tuning to Reward Learning

Calarina Muslimani, Yunshu Du, Kenta Kawamoto, Kaushik Subramanian, Peter Stone, Peter Wurman

原文摘要

The success of RL is fundamentally tied to having a reward function that accurately reflects the task objective. Yet, designing reward functions is time-consuming and prone to misspecification. To address this, our first goal is to understand how to support RL practitioners in specifying reward functions. We leverage the Trajectory Alignment Coefficient (TAC), a metric that evaluates how closely a reward function's induced preferences match those of a domain expert. To test whether TAC provides meaningful support, we evaluated its impact in a user study where practitioners designed reward functions for Lunar Lander. We found that iterative TAC feedback allowed participants to design more performant reward functions while reporting lower cognitive workload than unguided reward design. However, the study also underscored that manual reward design, even with TAC, remains labor-intensive. This motivated our second goal: to learn a reward model that maximizes TAC directly. Specifically, we propose Soft-TAC, a differentiable approximation of TAC that can be used as a loss function to train reward models from human preferences. Our theoretical analysis guarantees that under Soft-TAC optimization: (1) the optimal reward function belongs exclusively to the set of human-aligned reward functions, and (2) this optimal reward remains invariant whether trained on clean or noisy preference data. Empirically, we first evaluate reward models trained with Soft-TAC across three robotic tasks, demonstrating that they consistently match or outperform those trained with the Cross-Entropy loss. Next, we evaluate Soft-TAC in the GT7 driving simulator, and find that Soft-TAC-trained reward models more successfully capture preference-specific driving styles. Ultimately, this work demonstrates that TAC can serve as both a practical tool for guiding reward design and a reward learning objective in complex domains.

cs.LG · cs.HC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.00642

Coachable Agents for Interactive Gameplay

Roberto Capobianco, Harm van Seijen, Nolan D. Bard, Neil Burch, Fatima Davelouis, Josh Davidson, Alisa Devlic, Yunshu Du 等 41 位作者

原文摘要

Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing (Silver et al., 2016; 2017; Wurman et al., 2022) to robotics (Levine et al., 2016; Andrychowicz et al., 2020; Luo et al., 2025; Dürr et al., 2026) to foundation models (Ouyang et al., 2022; Noukhovitch et al., 2024; Sun et al., 2024; Guo et al., 2025). Through trial and error, these AI systems typically learn a single, near-optimal behavior to solve their tasks. However, there are many use cases in which one would like to assert some level of control, after training is completed, over how the task is solved. We refer to these modifications of a core task as styles. We combine universal value function approximators (UVFAs) with carefully selected training scenarios, learning algorithms, and data augmentation to create a framework for coaching agents that exhibit styles in complex domains. We demonstrate the framework's application in the AAA video games Horizon Forbidden West and Gran Turismo, and in an open-source humanoid test domain. Despite the different nature of the domains--car racing, stylized game combat, and humanoid walking--each agent shows strong coherence to the style requests while still satisfying the main task in its domain. Importantly, the techniques outlined in this paper allow an end user to choose the final behavior at run time, giving them flexible control over the final executed performance.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05571

ELA: End-to-End Latent Action Policy Learning from Video Demonstrations

Cheng-Chun Hsu, Peter Stone

原文摘要

Robot imitation learning is limited by the scarcity of action-labeled data, while action-free demonstrations such as videos are abundant. Recent latent-action methods learn action-relevant representations from observation transitions, but they do not optimize latent actions for downstream control, leading to latent representations that are difficult for policies to model and difficult to accurately decode into continuous robot actions. This paper introduces ELA, an end-to-end framework for learning from video demonstrations via latent actions jointly optimized for policy learning and action decoding. ELA jointly trains a latent action model, latent action policy, and action decoder, enabling policies to leverage abundant video data with only a handful of action-labeled robot demonstrations. Experiments on simulated and real-world manipulation tasks show that ELA consistently outperforms prior latent action baselines. Moreover, it integrates seamlessly with existing imitation learning methods through lightweight 4M-parameter action decoders, requiring no changes to policy architectures or training objectives. Additional materials can be found on: https://end2end-latent-action.github.io/

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34799

Centralized Critics for N-Agent Ad Hoc Teamwork

Andrew McAlinden, Caroline Wang, Steven Shi, Arrasy Rahman, Elad Liebman, Peter Stone

原文摘要

Centralized training with decentralized execution (CTDE) has become the dominant paradigm in multi-agent reinforcement learning (MARL), motivating extensive study of what privileged information should be made available to the critic during training. While prior work has examined the role of global state in fully cooperative settings, the design of centralized critics for N-agent ad hoc teamwork - in which dynamically varying set of learning agents must coordinate with an arbitrary set of uncontrolled teammates drawn from an unknown distribution - remains poorly understood. We study this question by comparing two natural classes of advantage estimators: controlled history estimators, which condition only on the observation and action histories of the learner's own agents, and joint history estimators, which additionally condition on the histories of the uncontrolled teammates. We prove that both estimators are unbiased and characterize the conditions under which the joint history estimate attains strictly lower variance than its controlled counterpart. Empirically, joint history critics match or outperform controlled history critics across a range of NAHT tasks, yielding improved sample efficiency and final returns. Together, these results provide both a theoretical justification and an empirical recommendation for incorporating uncontrolled agent histories into centralized critics for N-agent ad hoc teamwork.

cs.AI · cs.LG

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arXiv ↗PDF ↗