This paper deals with the concept of multi-robot task allocation, referring to the assignment of multiple robots to tasks such that an objective function is maximized. The performance of existing meta-heuristic methods worsens as the number of robots or tasks increases. To tackle this problem, a novel Markov decision process formulation for multi-robot task allocation is presented for reinforcement learning. The proposed formulation sequentially allocates robots to tasks to minimize the total time taken to complete them. Additionally, we propose a deep reinforcement learning method to find the best allocation schedule for each problem. Our method adopts the cross-attention mechanism to compute the preference of robots to tasks. The experimental results show that the proposed method finds better solutions than meta-heuristic methods, especially when solving large-scale allocation problems.

Recently, the use of quadrotors has increased in numerous applications, such as agriculture, rescue, transportation, inspection, and localization. Time-optimal quadrotor waypoint tracking is defined as controlling quadrotors to follow the given waypoints as quickly as possible. Although PID control is widely used for quadrotor control, it is not adaptable to environmental changes, such as various trajectories and dynamic external disturbances. In this work, we discover that adjusting PID control frequencies is necessary for adapting to environmental changes by showing that the optimal control frequencies can be different for different environments. Therefore, we suggest a method to schedule the PID position and attitude control frequencies for time-optimal quadrotor waypoint tracking. The method includes (1) a Control Frequency Agent (CFA) that finds the best control frequencies in various environments, (2) a Quadrotor Future Predictor (QFP) that predicts the next state of a quadrotor, and (3) combining the CFA and QFP for time-optimal quadrotor waypoint tracking under unknown external disturbances. The experimental results prove the effectiveness of the proposed method by showing that it reduces the travel time of a quadrotor for waypoint tracking.

Multi-agent reinforcement learning (MARL) has demonstrated significant potential in enabling cooperative agents. The communication protocol, which is responsible for message exchange between agents, is crucial in cooperation. However, communicative MARL systems still face challenges due to the noisy messages in complex multi-agent decision processes. This issue often stems from the entangled representation of observations and messages in policy networks. To address this, we propose the Message Action Adapter Framework (MAAF), which first trains individual agents without message inputs and then adapts a residual action based on message components. This separation isolates the effect of messages on action inference. We explore how training the MAAF framework with model-agnostic message types and varying optimization strategies influences adaptation performance. The experimental results indicate that MAAF achieves competitive performance across multiple baselines despite utilizing only half of the available communication, and shows an average improvement of 7.6% over the full attention-based communication approach. Additional findings suggest that different message types result in significant performance variations, emphasizing the importance of environment-specific message types. We demonstrate how the proposed architecture separates communication channels, effectively isolating message contributions.

Bumjin Park, Leejinsil Leejinsil, Jaesik Choi. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

An environment is a complex space in which numerous agents and objects interact. In this environment, rational agents act to maximize utility, and their actions are learned through reinforcement learning algorithms using deep neural networks. Since most environments are non-stationary, a single agent cannot solely achieve maximum utility through its observations and inferences using a network. However, when multiple agents can communicate with each other, a single agent can access richer information about the state of the environment simply by receiving messages from other agents. Nonetheless, the inferred information from other agents is generated by a neural network, which can be vulnerable or incorporate the personal perspective of the agents processing the information. For example, even when presented with a single observation, two agents can encode significantly different messages due to variations in their parameter modules. Therefore, it is crucial to consider the subjectivity of messages in multi-agent reinforcement learning (MARL). Building on the assumption that the encoded information is inherently subjective and reflects the agent that encodes it, we address the challenge of objective and subjective information in MARL. One limitation is that the information processed by a neural network is often challenging to interpret. In the initial phases of addressing this subjectivity concern, we focus on a relatively minor challenge: determining whether subjective information surpasses objective information in utility. Consequently, we compare the usefulness of messages exchanged between agents in two forms: raw observation and the output of a neural network.