Modeling an opponent's intent is critical for effective decision-making in non-cooperative, competitive, and general-sum multi-agent reinforcement learning. Existing opponent modeling methods encode intent using an embedding derived from episode information chosen a priori, such as the opponent's next action or a future environment state, and use this to guide the ego-agent's behavior. These approaches assume that the chosen information is universally representative of intent; however, we show empirically that this is not the case as intentions are often task- and environment-dependent. To address this, we introduce a task-adaptive opponent modeling framework that learns a performance-driven mixture of multiple intent representations. We further introduce a new intention representation that maximizes mutual information with the ego-agent's future returns, thereby capturing opponent information that is most directly relevant to performance. Our approach consistently matches or exceeds the performance of state-of-the-art baselines across diverse tasks and yields insights into when and why different opponent modeling strategies succeed.
Mario Gomez Andreu, Michael Amir, Antonio Marino, Tianxu An, Marco Hutter, Amanda Prorok
原文摘要
We consider the problem of sequential task continuation, where one robot acts in a workspace that has been affected by a previous robot. Current approaches in multi-robot cooperation rely heavily on co-training or co-optimization, assumingthat the robots have access to collective interaction data during the policy training or planning process. However, as increasingly varied robotic embodiments and policies are deployed and operate in the same real-world environment, learning policies for cooperation becomes infeasible due to the combinatorial explosion of possible robot combinations that need to be accounted for. To address this challenge, this paper considers the problem of ad hoc sequential cooperation between robots that have never before operated together in a shared environment. We demonstrate that robots can explicitly communicate their environmental preferences through sharing their value functions, enabling coordination without the need for prior joint training. To ground this exchange and prevent overconfidence in out-of-distribution states, we further introduce a k-nearest neighbors (KNN)- based uncertainty filter. Our method yields an increase of up to 48.5 pp compared with naive or even selfish agents, in task success rates across simulated and real-world experiments, demonstrating that value function transmission between agents is useful not merely as an internal learning tool, but as an effective basis for ad hoc coordination. Website: https://proroklab.github.io/PAVE/