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Ross Allen

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3 papers

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AAMAS Conference 2022 Conference Paper

Autonomous Flight Arcade Challenge: Single- and Multi-Agent Learning Environments for Aerial Vehicles

Paul Tylkin
Tsun-Hsuan Wang
Tim Seyde
Kyle Palko
Ross Allen
Alexander Amini
Daniela Rus

The Autonomous Flight Arcade (AFA) is a novel suite of singleand multi-agent learning environments for control of aerial vehicles. These environments incorporate realistic physics using the Unity game engine with diverse objectives and levels of decisionmaking sophistication. In addition to the environments themselves, we introduce an interface for interacting with them, including the ability to vary key parameters, thereby both changing the difficulty and the core challenges. We also introduce a pipeline for collecting human gameplay within the environments. We demonstrate the performance of artificial agents in these environments trained using deep reinforcement learning, and also motivate these environments as a benchmark for designing non-learned classical control policies and agents trained using imitation learning from human demonstrations. Finally, we motivate the use of AFA environments as a testbed for training artificial agents capable of cooperative human-AI decision making, including parallel autonomy.

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AAMAS Conference 2021 Conference Paper

Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning

Sheng Li
Jayesh K. Gupta
Peter Morales
Ross Allen
Mykel J. Kochenderfer

Multi-agent reinforcement learning (MARL) requires coordination to efficiently solve certain tasks. Fully centralized control is often infeasible in such domains due to the size of joint action spaces. Coordination graph formalizations allow reasoning about the joint action based on the structure of interactions. However, they often require domain expertise in their design and can be difficult for dynamic environments with changing coordination requirements. This paper introduces the deep implicit coordination graph (DICG) architecture for such scenarios. DICG consists of a module for inferring the dynamic coordination graph structure which is then used by a graph neural network module to learn to implicitly reason about the joint actions or values. DICG allows learning the tradeoff between full centralization and decentralization via standard actor-critic methods to significantly improve coordination for domains with large number of agents. We apply DICG to both centralized-training-centralized-execution and centralized-trainingdecentralized-execution regimes. We demonstrate that DICG solves the relative overgeneralization pathology in predatory-prey tasks as well as outperforms various MARL baselines on the challenging StarCraft II Multi-agent Challenge (SMAC) and traffic junction environments.

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NeurIPS Conference 2021 Conference Paper

Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi

Ho Chit Siu
Jaime Peña
Edenna Chen
Yutai Zhou
Victor Lopez
Kyle Palko
Kimberlee Chang
Ross Allen

Deep reinforcement learning has generated superhuman AI in competitive games such as Go and StarCraft. Can similar learning techniques create a superior AI teammate for human-machine collaborative games? Will humans prefer AI teammates that improve objective team performance or those that improve subjective metrics of trust? In this study, we perform a single-blind evaluation of teams of humans and AI agents in the cooperative card game Hanabi, with both rule-based and learning-based agents. In addition to the game score, used as an objective metric of the human-AI team performance, we also quantify subjective measures of the human's perceived performance, teamwork, interpretability, trust, and overall preference of AI teammate. We find that humans have a clear preference toward a rule-based AI teammate (SmartBot) over a state-of-the-art learning-based AI teammate (Other-Play) across nearly all subjective metrics, and generally view the learning-based agent negatively, despite no statistical difference in the game score. This result has implications for future AI design and reinforcement learning benchmarking, highlighting the need to incorporate subjective metrics of human-AI teaming rather than a singular focus on objective task performance.

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