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Title: Action Semantics Network: Considering the Effects of Actions in Multiagent Systems

Paper content:
In multiagent systems (MASs), each agent makes individual decisions but all of them contribute globally to the system evolution.
Learning in MASs is difficult since each agent's selection of actions must take place in the presence of other co-learning agents.
Moreover, the environmental stochasticity and uncertainties increase exponentially with the increase in the number of agents.
Previous works borrow various multiagent coordination mechanisms into deep learning architecture to facilitate multiagent coordination.
However, none of them explicitly consider action semantics between agents that different actions have different influences on other agents.
In this paper, we propose a novel network architecture, named Action Semantics Network (ASN), that explicitly represents such action semantics between agents.
ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them.
ASN can be easily combined with existing deep reinforcement learning (DRL) algorithms to boost their performance.
Experimental results on StarCraft II micromanagement and Neural MMO show ASN significantly improves the performance of state-of-the-art DRL approaches compared with several network architectures.
Deep reinforcement learning (DRL) (Sutton & Barto, 2018) has achieved a lot of success at finding optimal policies to address single-agent complex tasks (Mnih et al., 2015; Silver et al., 2017) .
However, there also exist a lot of challenges in multiagent systems (MASs) since agents' behaviors are influenced by each other and the environment exhibits more stochasticity and uncertainties (Claus & Boutilier, 1998; Hu & Wellman, 1998; Bu et al., 2008; Hauwere et al., 2016) .
Recently, a number of deep multiagent reinforcement learning (MARL) approaches have been proposed to address complex multiagent problems, e.g., coordination of robot swarm systems (Sosic et al., 2017) and autonomous cars (Oh et al., 2015) .
One major class of works incorporates various multiagent coordination mechanisms into deep multiagent learning architecture (Lowe et al., 2017; Yang et al., 2018; Palmer et al., 2018) .
Lowe et al. (2017) proposed a centralized actor-critic architecture to address the partial observability in MASs.
They also incorporate the idea of joint action learner (JAL) (Littman, 1994) to facilitate multiagent coordination.
Later, Foerster et al. (2018) proposed Counterfactual Multi-Agent Policy Gradients (COMA) motivated from the difference reward mechanism (Wolpert & Tumer, 2001) to address the challenges of multiagent credit assignment.
Recently, Yang et al. (2018) proposed applying mean-field theory (Stanley, 1971) to solve large-scale multiagent learning problems.
More recently, Palmer et al. (2018) extended the idea of leniency (Potter & Jong, 1994; Panait et al., 2008) to deep MARL and proposed the retroactive temperature decay schedule to address stochastic rewards problems.
However, all these works ignore the natural property of the action influence between agents, which we aim to exploit to facilitate multiagent coordination.
Another class of works focus on specific network structure design to address multiagent learning problems (Sunehag et al., 2018; Rashid et al., 2018; Sukhbaatar et al., 2016; Singh et al., 2019) .
Sunehag et al. (2018) designed a value-decomposition network (VDN) to learn an optimal linear value decomposition from the team reward signal based on the assumption that the joint actionvalue function for the system can be additively decomposed into value functions across agents.
Later, Rashid et al. (2018) relaxed the linear assumption in VDN by assuming that the Q-values of individual agents and the global one are also monotonic, and proposed QMIX employing a network that estimates joint action-values as a complex non-linear combination of per-agent values.
Recently, proposed the relational deep RL to learn environmental entities relations.
However, they considered the entity relations on the pixel-level of raw visual data, which ignores the natural property of the influence of actions between agents.
Tacchetti et al. (2019) proposed a novel network architecture called Relational Forward Model (RFM) for predictive modeling in multiagent learning.
RFM takes a semantic description of the state of an environment as input, and outputs either an action prediction for each agent or a prediction of the cumulative reward of an episode.
However, RFM does not consider from the perspective of the influence of each action on other agents.
OpenAI designed network structures to address multiagent learning problems in a famous Multiplayer Online Battle Arena (MOBA), Dota2.
They used a scaled-up version of PPO (Schulman et al. (2017) ), adopted the attention mechanism to compute the weight of choosing the target unit, with some of information selected from all information as input.
However, this selection is not considered from the influence of each action on other agents.
There are also a number of works designing network structures for multiagent communication (Sukhbaatar et al., 2016; Singh et al., 2019) .
However, none of the above works explicitly leverage the fact that an agent's different actions may have different impacts on other agents, which is a natural property in MASs and should be considered in the decision-making process.
In multiagent settings, each agent's action set can be naturally divided into two types: one type containing actions that affect environmental information or its private properties and the other type containing actions that directly influence other agents (i.e., their private properties).
Intuitively, the estimation of performing actions with different types should be evaluated separately by explicitly considering different information.
We refer to the property that different actions may have different impacts on other agents as action semantics.
We can leverage the action semantics information to improve an agent's policy/Q network design toward more efficient multiagent learning.
To this end, we propose a novel network architecture, named Action Semantics Network (ASN) to characterize such action semantics for more efficient multiagent coordination.
The main contributions of this paper can be summarized as follows:
1) to the best of our knowledge, we are the first to explicitly consider action semantics and design a novel network to extract it to facilitate learning in MASs;
2) ASN can be easily combined with existing DRL algorithms to boost its learning performance;
3) experimental results * on StarCraft II micromanagement (Samvelyan et al., 2019) and Neural MMO (Suarez et al., 2019) show our ASN leads to better performance compared with state-of-the-art approaches in terms of both convergence speed and final performance.
We propose a new network architecture, ASN, to facilitate more efficient multiagent learning by explicitly investigating the semantics of actions between agents.
To the best of our knowledge, ASN is the first to explicitly characterize the action semantics in MASs, which can be easily combined with various multiagent DRL algorithms to boost the learning performance.
ASN greatly improves the performance of state-of-the-art DRL methods compared with a number of network architectures.
In this paper, we only consider the direct action influence between any of two agents.
As future work, it is worth investigating how to model the action semantics among more than two agents.
Another interesting direction is to consider the action semantics between agents in continuous action spaces.
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assistant: text: Our proposed ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them.