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Title: Multi-Agent Interactions Modeling with Correlated Policies

Paper content:
In multi-agent systems, complex interacting behaviors arise due to the high correlations among agents.
However, previous work on modeling multi-agent interactions from demonstrations is primarily constrained by assuming the independence among policies and their reward structures.
In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents’ policies, which can recover agents' policies that can regenerate similar interactions.
Consequently, we develop a Decentralized Adversarial Imitation Learning algorithm with Correlated policies (CoDAIL), which allows for decentralized training and execution.
Various experiments demonstrate that CoDAIL can better regenerate complex interactions close to the demonstrators and outperforms state-of-the-art multi-agent imitation learning methods.
Our code is available at \url{https://github.com/apexrl/CoDAIL}.
Modeling complex interactions among intelligent agents from the real world is essential for understanding and creating intelligent multi-agent behaviors, which is typically formulated as a multiagent learning (MAL) problem in multi-agent systems.
When the system dynamics are agnostic and non-stationary due to the adaptive agents with implicit goals, multi-agent reinforcement learning (MARL) is the most commonly used technique for MAL.
MARL has recently drawn much attention and achieved impressive progress on various non-trivial tasks, such as multi-player strategy games (OpenAI, 2018; Jaderberg et al., 2018) , traffic light control (Chu et al., 2019) , taxi-order dispatching etc.
A central challenge in MARL is to specify a good learning goal, as the agents' rewards are correlated and thus cannot be maximized independently (Bu et al., 2008) .
Without explicit access to the reward signals, imitation learning could be the most intuitive solution for learning good policies directly from demonstrations.
Conventional solutions such as behavior cloning (BC) (Pomerleau, 1991) learn the policy in a supervised manner by requiring numerous data while suffering from compounding error (Ross & Bagnell, 2010; Ross et al., 2011) .
Inverse reinforcement learning (IRL) (Ng et al., 2000; Russell, 1998) alleviates these shortcomings by recovering a reward function but is always expensive to obtain the optimal policy due to the forward reinforcement learning procedure in an inner loop.
Generative adversarial imitation learning (GAIL) (Ho & Ermon, 2016 ) leaves a better candidate for its model-free structure without compounding error, which is highly effective and scalable.
However, real-world multi-agent interactions could be much challenging to imitate because of the strong correlations among adaptive agents' policies and rewards.
Consider if a football coach wants to win the league, he must make targeted tactics against various opponents, in addition to the situation of his team.
Moreover, the multi-agent environment tends to give rise to more severe compounding errors with more expensive running costs.
Motivated by these challenges, we investigate the problem of modeling complicated multi-agent interactions from a pile of off-line demonstrations and recover their on-line policies, which can regenerate analogous multi-agent behaviors.
Prior studies for multi-agent imitation learning typically limit the complexity in demonstrated interactions by assuming isolated reward structures (Barrett et al., 2017; Le et al., 2017; Lin et al., 2014; Waugh et al., 2013) and independence in per-agent policies that overlook the high correlations among agents (Song et al., 2018; Yu et al., 2019) .
In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework with correlated policies by approximating opponents' policies, in order to reach inaccessible opponents' actions due to concurrently execution of actions among agents when making decisions.
Consequently, with approximated opponents model, we develop a Decentralized Adversarial Imitation Learning algorithm with Correlated policies (CoDAIL) suitable for learning correlated policies under our proposed framework, which allows for decentralized training and execution.
We prove that our framework treats the demonstrator interactions as one of -Nash Equilibrium ( -NE) solutions under the recovered reward.
In experiments, we conduct multi-dimensional comparisons for both the reward gap between learned agents and demonstrators, along with the distribution divergence between demonstrations and regenerated interacted trajectories from learned policies.
Furthermore, the results reveal that CoDAIL can better recover correlated multi-agent policy interactions than other state-of-the-art multi-agent imitation learning methods in several multi-agent scenarios.
We further illustrate the distributions of regenerated interactions, which indicates that CoDAIL yields the closest interaction behaviors to the demonstrators.
In this paper, we focus on modeling complex multi-agent interactions via imitation learning on demonstration data.
We develop a decentralized adversarial imitation learning algorithm with correlated policies (CoDAIL) with approximated opponents modeling.
CoDAIL allows for decentralized training and execution and is more capable of modeling correlated interactions from demonstrations shown by multi-dimensional comparisons against other state-of-the-art multi-agent imitation learning methods on several experiment scenarios.
In the future, we will consider covering more imitation learning tasks and modeling the latent variables of policies for diverse multi-agent imitation learning.

assistant: text: Modeling complex multi-agent interactions under multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents’ policies.