arXiv subjects

Shuyue Hu

Publications and source records attributed to Shuyue Hu.

57 records · Page 4Linked to original sources

arXiv2022

Individual-Level Inverse Reinforcement Learning for Mean Field Games

The recent mean field game (MFG) formalism has enabled the application of inverse reinforcement learning (IRL) methods in large-scale multi-agent systems, with the goal of inferring reward signals that can explain demonstrated behaviours of large populations. The existing IRL methods for MFGs are built upon reducing an MFG to a Markov decision process (MDP) defined on the collective behaviours and average rewards of the population. However, this paper reveals that the reduction from MFG to MDP holds only for the fully cooperative setting. This limitation invalidates existing IRL methods on MFGs with non-cooperative environments. To measure more general behaviours in large populations, we study the use of individual behaviours to infer ground-truth reward functions for MFGs. We propose Mean Field IRL (MFIRL), the first dedicated IRL framework for MFGs that can handle both cooperative and non-cooperative environments. Based on this theoretically justified framework, we develop a practical algorithm effective for MFGs with unknown dynamics. We evaluate MFIRL on both cooperative and mixed cooperative-competitive scenarios with many agents. Results demonstrate that MFIRL excels in reward recovery, sample efficiency and robustness in the face of changing dynamics.

cs.LG↗

arXiv2020

A Q-values Sharing Framework for Multiagent Reinforcement Learning under Budget Constraint

In teacher-student framework, a more experienced agent (teacher) helps accelerate the learning of another agent (student) by suggesting actions to take in certain states. In cooperative multiagent reinforcement learning (MARL), where agents need to cooperate with one another, a student may fail to cooperate well with others even by following the teachers' suggested actions, as the polices of all agents are ever changing before convergence. When the number of times that agents communicate with one another is limited (i.e., there is budget constraint), the advising strategy that uses actions as advices may not be good enough. We propose a partaker-sharer advising framework (PSAF) for cooperative MARL agents learning with budget constraint. In PSAF, each Q-learner can decide when to ask for Q-values and share its Q-values. We perform experiments in three typical multiagent learning problems. Evaluation results show that our approach PSAF outperforms existing advising methods under both unlimited and limited budget, and we give an analysis of the impact of advising actions and sharing Q-values on agents' learning.

cs.MA↗

arXiv2020

The Evolutionary Dynamics of Independent Learning Agents in Population Games

Understanding the evolutionary dynamics of reinforcement learning under multi-agent settings has long remained an open problem. While previous works primarily focus on 2-player games, we consider population games, which model the strategic interactions of a large population comprising small and anonymous agents. This paper presents a formal relation between stochastic processes and the dynamics of independent learning agents who reason based on the reward signals. Using a master equation approach, we provide a novel unified framework for characterising population dynamics via a single partial differential equation (Theorem 1). Through a case study involving Cross learning agents, we illustrate that Theorem 1 allows us to identify qualitatively different evolutionary dynamics, to analyse steady states, and to gain insights into the expected behaviour of a population. In addition, we present extensive experimental results validating that Theorem 1 holds for a variety of learning methods and population games.

cs.MA↗