Multi-Agent Generative Adversarial Imitation Learning (1807.09936v1)

Abstract: Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal. However, most existing approaches are not applicable in multi-agent settings due to the existence of multiple (Nash) equilibria and non-stationary environments. We propose a new framework for multi-agent imitation learning for general Markov games, where we build upon a generalized notion of inverse reinforcement learning. We further introduce a practical multi-agent actor-critic algorithm with good empirical performance. Our method can be used to imitate complex behaviors in high-dimensional environments with multiple cooperative or competing agents.

• The paper's primary contribution is introducing the MAGAIL framework for multi-agent imitation learning, extending inverse RL concepts to handle non-stationarity and multiple equilibria.
• It integrates temporal difference learning with a multi-agent actor-critic approach using K-FAC to stabilize policy gradients in both cooperative and competitive scenarios.
• Empirical evaluations demonstrate that MAGAIL outperforms behavior cloning in cooperative tasks and adapts effectively in competitive environments.

Insights into Multi-Agent Generative Adversarial Imitation Learning

This paper investigates the extension of imitation learning to multi-agent systems, addressing challenges inherent in environments characterized by non-stationarity and multiple equilibria as typically encountered in multi-agent settings. The authors propose a framework that generalizes the principles of inverse reinforcement learning to accommodate scenarios involving numerous agents, as is the case in Markov games.

The primary contribution of this paper is the introduction of a multi-agent Generative Adversarial Imitation Learning (MAGAIL) framework. This methodology expands upon the single-agent Generative Adversarial Imitation Learning (GAIL) concept, effectively enabling the imitation of complex behaviors in environments with multiple cooperative or competing agents. Key to this approach is the integration of multi-agent reinforcement learning (MARL) with an extension of multi-agent inverse RL.

The proposed algorithm uses a two-player game paradigm between generators and discriminators akin to adversarial networks. The generator controls the distributed policies across all agents, while individual discriminators are tasked with distinguishing the agents' behaviors against the expert demonstrations. This adversarial training paradigm is highlighted by its foundation in matching occupancy measures between policy-produced and expert behaviors.

The methodological structure introduces several advancements:

1. Temporal Difference Learning Integration: Enhanced understanding of Nash equilibrium in the multi-agent context is achieved via constraints reformulated through temporal difference learning, simplifying the Lagrangian solution.
2. Multi-Agent Actor-Critic Optimization: Utilizing a centralized training with decentralized execution approach, the algorithm applies the Kronecker-factored trust region (K-FAC) for scalable natural policy gradient optimization, demonstrated to effectively tackle issues of high variance in policy gradients typical in multi-agent scenarios.

Empirical evaluations underscore the efficacy of MAGAIL in diverse environments. In cooperative tasks, versions of MAGAIL delivered superior performance over behavior cloning (BC) techniques, achieving close approximations to expert-level demonstrations with fewer samples. In competitive environments, the framework’s adaptability, attributed to its specified reward structure priors, showcased notable advantages over centralized approaches.

These results position MAGAIL as a robust imitation learning method in multi-agent settings, by balancing complexity and computational efficiency. Future prospects include refining cooperative and competitive agent interactions in even more complex scenarios, enhancing the scalability of the algorithm, and deep integration with advanced reinforcement learning techniques. This research opens the door to further explorations in realistic applications where multi-agent interactions are pivotal.