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ASN: action semantics network for multiagent reinforcement learning

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Abstract

In multiagent systems (MASs), each agent makes individual decisions but all contribute globally to the system’s 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 number of agents. Previous works borrow various multiagent coordination mechanisms for use in deep learning architectures to facilitate multiagent coordination. However, none of them explicitly consider that different actions can have different influence on other agents, which we call the action semantics. 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 that ASN significantly improves the performance of state-of-the-art DRL approaches, compared with several other network architectures. We also successfully deploy ASN to a popular online MMORPG game called Justice Online, which indicates a promising future for ASN to be applied in even more complex scenarios.

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Notes

  1. More details can be found at https://sites.google.com/view/asn-intro, the source code is at https://github.com/wwxFromTju/ASN_cloud

  2. https://github.com/wwxFromTju/MA-RLlib

  3. Our ASN is compatible with extensions of QMIX since these methods follow the QMIX structure and we select QMIX as an representative baseline.

  4. The implementation details are not public since this is a commercial game and all details are close-sourced.

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Acknowledgements

This work is supported by the Major Program of the National Natural Science Foundation of China(Grant No. 92370132) and the National Key R&D Program of China (Grant No. 2022ZD0116402). Part of this work has taken place in the Intelligent Robot Learning (IRL) Lab at the University of Alberta, which is supported in part by research grants from the Alberta Machine Intelligence Institute (Amii); a Canada CIFAR AI Chair, Amii; Compute Canada; Huawei; Mitacs; and NSERC.

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  1. Tianpei Yang and Weixun Wang have contributed equally to this work.

Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Tianpei Yang, Jianye Hao & Xiaotian Hao

  2. Department of Computing Science, University of Alberta, Edmonton, Canada

    Tianpei Yang & Matthew E. Taylor

  3. Alberta Machine Intelligence Institute (Amii), Edmonton, Canada

    Tianpei Yang & Matthew E. Taylor

  4. Fuxi AI Lab, NetEase, Hangzhou, China

    Weixun Wang, Yujing Hu, Yingfeng Chen, Changjie Fan, Chunxu Ren & Ye Huang

  5. Huawei, Shenzhen, China

    Jianye Hao & Jiangcheng Zhu

  6. Nanjing University, Nanjing, China

    Yong Liu & Yang Gao

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  1. Tianpei Yang
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TY wrote the main manuscript text. WW and TY prepared all experimental results. All authors reviewed the manuscript.

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Appendices

Appendix A: environmental settings

1.1 A.1 StarCraft II

State Description In StarCraft II, we follow the settings of previous works [15, 22]. The local observation of each agent is drawn within their field of view, which encompasses the circular area of the map surrounding units and has a radius equal to the sight range. Each agent receives as input a vector consisting of the following features for all units in its field of view (both allied and enemy): distance, relative x, relative y, and unit type. More details can be found at https://github.com/wwxFromTju/ASN_cloud or https://github.com/oxwhirl/smac.

1.2 A.2 Neural MMO

State Description In a 10 × 10 tile (where each tile can be set as different kinds, e.g., rocks, grass), there are two teams of agents (green and red), each of which has 3 agents. At the beginning of each episode, each agent appears on any of the 10 × 10 tiles. The observation of an agent is in the form of a 43-dimensional vector, in which the first 8 dimensions are: time to live, HP, remaining foods (set 0), remaining water (set 0), current position (x and y), the amount of damage suffered, frozen state (1 or 0); the rest of 35 dimensions are divided equally to describe the other 5 agents’ information. The first 14 dimensions describe the information of 2 teammates, followed by the description of 3 opponents’ information. Each observed agent’s information includes the relative position (x and y), whether it is a teammate (1 or 0), HP, remaining foods, remaining water, and the frozen state.

Each agent chooses an action from a set of 14 discrete actions: stop, move left, right, up or down, and three different attacks (“Melee”, the attack distance is 2, the damage is 5; “Range”, the attack distance is 4, the damage is 2; “Mage”, the attack distance is 10, the damage is 1) against one of the three opponents.

Each agent gets a penalty of \(-0.1\) if the attack fails. They get a \(-0.01\) reward for each tick and a \(-10\) penalty for being killed. The game ends when a group of agents dies or the time exceeds a fixed period, and agents belonging to the same group receive the same reward, which is the difference of the total number of HPs between itself and its opposite side.

Appendix B: Network structure and parameter settings

1.1 B.1 StarCraft II

Network Structure The details of different network structures for StarCraft II are shown in Fig. 19. The vanilla network (Fig. 19a) of each agent i contains two fully-connected hidden layers with 64 units and one GRU layer with 64 units, taking \(o_t^i\) as input. The output layer is a fully-connected layer that outputs the Q-values of each action. The attention network (Fig. 19b) of each agent i contains two isolated fully-connected layers with 64 units, taking \(o_t^i\) as input and computing the standard attention value for each dimension of the input. The following hidden layer is a GRU with 64 units. The output contains the Q-values of each action. The entity-attention network (Fig. 19c) is similar to that in Fig. 19b, except that the attention weight is calculated on each \(o_t^{i,j}\). The dueling network (Fig. 19d) is the same as vanilla except for the output layer that outputs the advantages of each action and also the state value. Our homogeneous ASN (Fig. 19e) of each agent i contains two sub-modules, one is the \(O2A^i\) which contains two fully-connected layers with 32 units, taking \(o_t^i\) as input, followed by a GRU layer with 32 units; the other is a parameter-sharing sub-module which contains two fully-connected layers with 32 units, taking each \(o_t^{i,j}\) as input, following with a GRU layer with 32 units; the output layer outputs the Q-values of each action.

Fig. 19
figure 19

Various network structures on a StartCraft II 8m map

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Parameter Settings Here we provide the hyperparameters for StarCraft II as shown in Table 4, more details can be found at https://github.com/wwxFromTju/ASN_cloud.

Table 4 Hyperparameter settings for StarCraft II
Full size table

1.2 Neural MMO

Network structure The details of vanilla, attention, and entity-attention networks for Neural MMO are shown in Fig. 20a–c which contains an actor network, and a critic network. All actors are similar to those for StarCraft II in Fig. 19, except that the GRU layer is excluded and the output is the logic probability of choosing each action. All critics are the same as shown in Fig. 20a. Since in Neural MMO, each agent has multiple actions that have a direct influence on each other agent, i.e., three kinds of attack actions, we test two kinds of ASN variants: one (Fig. 20d) is the Multi-action ASN we mentioned in the previous section that shares the first layer parameters among multiple actions; the other (Fig. 20e) is the basic homogeneous ASN that does not share the first layer parameters among multiple actions.

Fig. 20
figure 20

Various network structures on Neural MMO

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Parameter settings Here we provide the hyperparameters for Neural MMO shown in Table 5.

Table 5 Parameters of all algorithms
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Appendix C: Environmental results

The following results present the performance of QMIX-ASN and vanilla QMIX under different StarCraft II maps by adding the manual rule (forbids the agent to choose the invalid actions) (Fig. 21).

Fig. 21
figure 21

Win rates of QMIX-ASN and vanilla QMIX on 5 m and 8 m StarCraft II maps

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Yang, T., Wang, W., Hao, J. et al. ASN: action semantics network for multiagent reinforcement learning. Auton Agent Multi-Agent Syst 37, 45 (2023). https://doi.org/10.1007/s10458-023-09628-3

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