Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations

Effective communication is an important skill for enabling information exchange and cooperation in multi-agent settings . Indeed , emergent communication is now a vibrant field of research , with common settings involving discrete cheap-talk channels . One limitation of this setting is that it does not allow for the emergent protocols to generalize beyond the training partners . Furthermore , so far emergent communication has primarily focused on the use of symbolic channels . In this work , we extend this line of work to a new modality , by studying agents that learn to communicate via actuating their joints in a 3D environment . We show that under realistic assumptions , a non-uniform distribution of intents and a commonknowledge energy cost , these agents can find protocols that generalize to novel partners . We also explore and analyze specific difficulties associated with finding these solutions in practice . Finally , we propose and evaluate initial training improvements to address these challenges , involving both specific training curricula and providing the latent feature that can be coordinated on during training . 1 INTRODUCTION . The ability to communicate effectively with other agents is part of a necessary skill repertoire of intelligent agents and , by definition , can only be studied in multi-agent contexts . Over the last few years , a number of papers have studied emergent communication in multi-agent settings ( Lazaridou et al. , 2016 ; Havrylov & Titov , 2017 ; Cao et al. , 2018 ; Bouchacourt & Baroni , 2018 ; Eccles et al. , 2019 ; Graesser et al. , 2019 ; Chaabouni et al. , 2019 ; Lowe et al. , 2019b ) . This work typically assumes a symbolic ( discrete ) cheap-talk channel , through which agents can send messages that have no impact on the reward function or transition dynamics . A common task is the so called referential game , in which a sender observes an intent needing to be communicated to a listener via a message . In these cheap-talk settings , the solution space typically contains many equivalent but mutually incompatible ( self-play ) policies . For example , permuting bits in the channel and adapting the receiver policy accordingly would preserve payouts , but differently permuted senders and receivers are mutually incompatible . This makes it difficult for independently trained agents to utilize the cheap-talk channel at test time , a setting which is formalized as zero-shot ( ZS ) coordination ( Hu et al. , 2020 ) . In contrast , we study how gesture-based communication can emerge under realistic assumptions . Specifically , this work considers emergent communication in the context of embodied agents that learn to communicate through actuating and observing their joints in simulated physical environments . In other words , our setup is a referential game , where each message is a multi-step process that produces an entire trajectory of limb motion ( continuous actions ) in a simulated 3D world . Not only does body language play a crucial role in social interactions , but furthermore , zoomorphic agents , robotic manipulators , and prelingual infants are generally not expected to use symbolic language to communicate at all . From a practical point of view , it is clear that our future AI agents will need to signal and interpret the body language of other ( human ) agents , e.g. , when self-driving cars decide whether it is safe to cross an intersection . With that , there has been work on the emergence of grounded language for robots ( Steels et al. , 2012 ; Spranger , 2016 ) . To the best of our knowledge however , we are first to explore deep reinforcement learning for emergent communication in the context of embodied agents using articulated motion . Moreover , while cheap-talk is a great proxy for symbolic communication across dedicated channels , communication through articulated motion means agents have to control their joints to signal . One universal feature of this physical actuation is it requires the expenditure of energy , which is a scarce resource both for biological agents and man-made robots . Another ubiquitous factor of the physical world ( and many other domains ) is that communicative intents are not distributed uniformly . In particular , the Zipf distribution ( Zipf , 2016 ) is known to be a good proxy for many real-world distributions associated with human activity . A consequence of combining energy cost with a non-uniform distribution over intents in the context of referential games is that , in principle , it allows for ZS communication : Trajectories requiring lower energy exertion should be used for encoding more common intents , while those associated with higher energy encode less common ones . In contrast , superficially related , auxiliary losses such as entropy penalties do not allow for ZS coordination without further assumptions . While these auxillary losses are design decisions , energy cost is an example of a universal ( common-knowledge ) cost grounded in the environment , which can be exploited for ZS communication . Unfortunately , training agents that can successfully learn these strategies is a difficult problem for current state-of-the-art machine learning models . There are three major challenges : 1 ) Local optima associated with the lock-in between sender and receiver : The interpretation of a message depends on the entire policy , not just the state and action . While recent methods have been developed to address this in discrete action spaces ( Foerster et al. , 2019 ) , to the best of our knowledge none have been proposed for costly , continuous action spaces . 2 ) The latent structure underlying the protocol , in our case energy , that can be coordinated on , needs to be discovered , requiring agents to ignore other ( redundant ) degrees of freedom . 3 ) Even when this structure is provided , optimization is difficult since it contains a large number of local optima . As a consequence , on top of the continuous optimization problem there is a combinatorial problem of ordering the energy values for each intent . We explore and analyse these difficulties , suggesting initial steps for addressing them in our setting . First , we show that providing the latent variable ( energy ) at training time does indeed allow for some amount of ZS coordination . To do so , we adapt our method two fold : 1 ) During training , we change the observation to only the energy value of a given trajectory . 2 ) We add an observer agent that trains on an entire population of fully trained Self-Play ( SP ) agents . To evaluate ZS performance , we test this observer on an independently trained set of SP agents . The ZS performance in this setting is around 35 % for 10 intents , comparable with the most frequent class for the Zipf-distribution ( 34 % ) . Next , we pretrain sender agents to minimize the energy associated with each intent , but ZS performance remains around 34 % ( 10 intents ) . This is intuitive due to the challenges associated with re-ordering energy values on a 1D line without incurring a high cross-entropy while the different distributions are overlapping . Finally , we show that using the entire trajectory during SP , but only the energy value for the external observer , in combination with pretraining , consistently achieves a much higher ZS performance of around 56 % . All of this illustrates that learning an optimal ZS policy given a specific set of assumptions about the problem setting , which are common knowledge to all parties , is a challenging problem , even in seemingly simple instances . Furthermore , since this work focuses on the two extreme ends of this problem ( self-play and ZS ) , our ideas are relevant for a broad range of intermediate settings as well . 2 BACKGROUND . 2.1 MULTI-AGENT REINFORCEMENT LEARNING . We formalize the protocol learning problem as a decentralized partially observable Markov decision process with N agents ( Bernstein et al. , 2002 ) , defined by tuple ( S , A , T , R , O , Ω , γ ) . S is the set of states , A1 · · ·AN the set of actions for each of N agents in the population , and T a transition function S×A1 · · ·AN→ S mapping each state and set of agent actions taken to a distribution over next states . This work assumes all agents have identical state and action spaces . In a partially observable setting , no agent can directly observe the underlying state s , but each receives a private observation oi ∈ Ω correlated with the state . O ( o|a , s′ ) is the probability that o is observed , given action a led to state s′ . Agent reward ri : Ω×A1 · · ·AN→R is a function of state and actions taken . The objective is to infer a set of agent policies that maximize expected shared return R. We employ a centralized training regime with execution decentralized ( Foerster et al. , 2016 ; Lanctot et al. , 2017 ; Rashid et al. , 2018 ) . Policy gradient algorithms ( Williams , 1992 ; Sutton et al. , 2000 ) are widely used for reinforcement learning domains with continuous action spaces . Similar to ( Mordatch & Abbeel , 2018 ) , we use a model-based policy gradient approach with a fully differentiable dynamics model for our emergent communication framework . In particular , we employ a stochastic value gradient approach , SVGinfinity ( Heess et al. , 2015 ) . SVG methods compute the policy gradient through backpropagation . 2.2 ZERO-SHOT COORDINATION . Zero-shot ( ZS ) coordination is the problem setting of agents coordinating at test time with novel partners , i.e . other independently trained agents ( Hu et al. , 2020 ) . In cooperative multiagent settings , a key challenge is for agents to learn general skills for coordinating and communicating with other agents . Nonetheless , just as in single-agent settings , agents can overfit to their environment , in multiagent settings , agents can co-adapt with and overfit to their training partners . Thus ZS coordination is useful for evaluating how well the learned agent policies generalize to unseen agents they may need to later coordinate with ( e.g . new human partners ) . 3 PROBLEM SETTING . This work is framed in the ZS Communication problem setting . ZS communication is a specific instance of the ZS coordination setting , where the task for the agents is to communicate effectively ; there is no additional cooperative task being solved concurrently , in the environment . The protocol learning problem then for all independently trained sender-receiver agent pairs is to converge upon optimal zero-shot policies , for communicating intent to novel partners at test time . Sender-receiver pairs engage in self-play to train communication protocols , then we use cross-play as our evaluation metric for testing ZS coordination . Cross-play ( CP ) is an instance of out-of-distribution testing , whereby senders are paired with receivers they have never encountered nor influenced during training ( i.e . agent pairs tested in CP have been trained independently of one another ) . In our embodied problem formulation , populations are composed of spatially articulated agents . Concepts represent communicative intents and messages are instantiated as motion trajectories . Intents are represented as a discrete-valued symbols , defined a priori from an intent library . The policy state space S consists of the joint configuration of an agent and an intent to be communicated , where the joint configuration of the agent is defined by three-dimensional position ( px , py , pz ) and threedimensional rotation ( rx , ry , rz ) of each agent joint j in the agent ’ s set of joints J . The policy action space A consists of the angular velocities for each joint j ∈ J : ( ∆rx , ∆ry , ∆rz ) . All a ∈ A are communication actions which can be observed by other agents but have no effect on the environment . We employ referential games for generating communicative motion through paired-play . At each time step t , the policy inputs the joint configuration of the agent , ( px , py , pz , rx , ry , rz ) ∀ j ∈ J , concatenated with a fixed intent embedding . It outputs an action at , ( ∆rx , ∆ry , ∆rz ) ∀ j ∈ J. Transitions T : ( st , at ) → st+1 are deterministically computed through a differentiable forward kinematics ( FK ) module , ensuring kinematically valid trajectories are generated . After T steps , the episode terminates , and the sequence of joint states visited and velocity actions taken is concatenated to compose a motion trajectory . The observer model takes the motion trajectory as input and predicts the actor ’ s intent . The shared return for the episode is the cross-entropy loss between the observer ’ s prediction and the ground truth intent ; this return R is backpropagated from observer to actor , since we use a centralized training regime for the multi-agent system . The FK module has been adapted from an existing motion library ( Holden et al. , 2017 ) to be differentiable . Figure 1 illustrates the embodied referential game . Algorithm 1 provides an overview of training for the game . Importantly , for continuous domains , the injection of noise is critical . During trajectory generation , Gaussian noise is added to each action degree of freedom , both to represent imperfect observability and to induce exploration . Noise helps regularize the communication channel , in that a finite noise level essentially provides a quantization of the continuous space , a ’ coarse graining ’ , such that trajectories mapped to different intents are distinguishable . This prevents a collapse of the trajectories .

Summary: The paper investigates emergent gesture-based communication in Embodied Multi-Agent Populations. A noticeable feature of the paper is that it investigates emergent communication in the case of non-uniform distribution of intents and costly communication (i.e. agents are penalized for effort). The authors find that in certain scenarios, these conditions may lead to communication generalization of learned communication strategies to new, previously unseen agents.