--- title: "Goal Consistency: An Effective Multi-Agent Cooperative Method for Multistage Tasks" authors: - "Xinning Chen" - "Xuan Liu" - "Shigeng Zhang" - "Bo Ding" - "Kenli Li" doi: "10.24963/ijcai.2022/25" url: "https://ijcai.org/proceedings/2022/25" pdf: "https://ijcai.org/proceedings/2022/0025.pdf" venue: "Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence" year: 2022 pages: "172-178" track: "Main Track" keywords: - "Agent-based and Multi-agent Systems: Coordination and Cooperation" - "Agent-based and Multi-agent Systems: Multi-agent Learning" --- # Goal Consistency: An Effective Multi-Agent Cooperative Method for Multistage Tasks Xinning Chen, Xuan Liu, Shigeng Zhang, Bo Ding, Kenli Li *Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence*, pages 172-178. DOI: [10.24963/ijcai.2022/25](https://doi.org/10.24963/ijcai.2022/25) ## Abstract Although multistage tasks involving multiple sequential goals are common in real-world applications, they are not fully studied in multi-agent reinforcement learning (MARL). To accomplish a multi-stage task, agents have to achieve cooperation on different subtasks. Exploring the collaborative patterns of different subtasks and the sequence of completing the subtasks leads to an explosion in the search space, which poses great challenges to policy learning. Existing works designed for single-stage tasks where agents learn to cooperate only once usually suffer from low sample efficiency in multi-stage tasks as agents explore aimlessly. Inspired by human’s improving cooperation through goal consistency, we propose Multi-Agent Goal Consistency (MAGIC) framework to improve sample efficiency for learning in multi-stage tasks. MAGIC adopts a goal-oriented actor-critic model to learn both local and global views of goal cognition, which helps agents understand the task at the goal level so that they can conduct targeted exploration accordingly. Moreover, to improve exploration efficiency, MAGIC employs two-level goal consistency training to drive agents to formulate a consistent goal cognition. Experimental results show that MAGIC significantly improves sample efficiency and facilitates cooperation among agents compared with state-of-art MARL algorithms in several challenging multistage tasks. ## Full text ## 1 Introduction Multi-agent reinforcement learning (MARL) has shown superiority in cooperative multi-agent decision control problems, such as multi-player games, autonomous vehicles and traffic control [Gronauer and Diepold, 2021]. However, learning to cooperate becomes extremely difficult due to the combinatorial possibilities of agent interactions and the huge state space in increasingly complex multi-agent tasks, which may be even worse when the tasks contain multiple stages. Multistage tasks are common in real-word application, such as freight transportation tasks that require the cargo to pass through multiple delivery points in sequence according to the prescribed routes. Such multi-stage tasks require agents to cooperate to achieve multiple goals in sequence together. Exploring the collaborative patterns of different subtasks and the completion sequence of the subtasks leads to an explosion in the search space, which poses greater challenges to policy learning than in single-stage tasks. ∗These authors contributed equally to this work. †Corresponding author To promote cooperation in large state spaces, existing works mainly focus on optimizing a joint goal via sharing and enhancing information at the agent level. [Yang et al., 2018] reduces the input dimension by approximating the interactions within the population of agents with an average effect. NCC-Q [Mao et al., 2020] learns cognitive consistency of neighborhood observations to encourage collaboration. Attention mechanisms are widely used to simplify the learning process by discovering the relationship among agents [Iqbal and Sha, 2019; Mao et al., 2019; Ryu et al., 2020; Liu et al., 2020]. However, these algorithms struggle to learn optimal policies with aimless exploration in more complex multistage tasks with multiple goals. We observe that achieving goal consistency is a core factor of successful teamwork in human society. For cooperative tasks with multiple goals in the real world, people tend to reach a consensus on which goal to achieve first before taking action. Obviously, goal consistency helps avoid useless exploration and improves cooperation efficiency. In other words, when the goals pursued are inconsistent, human individuals are unlikely to form coordinated behaviors to complete tasks. Take wildlife rescue as an example. In this task, a group of rescuers needs to rescue several wild animals together based on the severity of their injuries. If rescuers do not reach a consensus on the assistance goals, the conflict will hinder their cooperation, leading to failure in rescuing any animals timely. In contrast, a teamwith a consistent goal can quickly determine the assistance goal and achieve precise rescue. Inspired by the above observation, we introduce goal consistency into multi-agent reinforcement learning and propose Multi-Agent Goal Consistency (MAGIC) framework to promote cooperation among agents in multistage tasks with multiple goals. We explicitly incorporate goal cognition into the decision-making process by designing a goal-oriented actorcritic model that learns to cognitive goals and dynamically focuses on different goals in different stages. Moreover, taking advantage of centralized training, we obtain goal cognition from local and global views, which further motivates the two-level goal consistency training. By keeping consistent with each other through self-supervised learning and aligning individual goal with that of other agents, each agent forms a consistent goal cognition in local view and global view to improve sample efficiency and conduct effective exploration, thus achieving efficient cooperation. We evaluate our methods on several challenging multistage tasks. Experiment results show that agents exhibit mutually cooperative behaviors with consistent goal cognition. Moreover, MAGIC significantly outperforms state-of-the-art MARL approaches in convergence speed and final cooperation performance. ## 2 RelatedWork Among existing MARL algorithms, sharing and collecting information about other agents is a common way to achieve consensus for better cooperation. Learning to share information by communication has been exploited to accelerate cooperation [Sukhbaatar et al., 2016; Jiang and Lu, 2018; Ding et al., 2020; Lin et al., 2021]. Under the paradigm of centralized training and decentralized execution (CTDE), MADDPG [Lowe et al., 2017] has extended DDPG [Lillicrap et al., 2016] to multi-agent settings by augmenting each agent with information about others in centralized critics. NCC-Q [Mao et al., 2020] imposes perceptual consensus constraints on the agent and other agents within its fixed neighborhood to encourage collaboration. Mean Filed [Yang et al., 2018] approximates the interaction within neighboring agents using an average effect to enable coordination between large-scale agents. Recent works explore the effectiveness of employing attention networks [Xu et al., 2015] to process the information shared by other agents. Multiple-actor-attentioncritic (MAAC) [Iqbal and Sha, 2019] introduces multi-head soft-attention into the centralized critics to focus on relevant agents' information. Multi-agent game abstraction algorithm [Liu et al., 2020] combines soft-attention and hard-attention mechanisms to learn the relationship between agents for effective cooperation. However, these methods tend to accomplish a joint goal via sharing and enhancing information at the agent level, which generally find difficulty to learn effective cooperation in large state space with aimless exploration. To improve sample efficiency, several attempts have been made to promote efficient exploration in MARL domain. IGASIL [Hao et al., 2019] guide agents to imitate from the past good experiences and do more exploration around these high-reward regions. Rochico [Li et al., 2021b] improves exploration efficiency based on reinforced organization control and hierarchical consensus learning. CM3 [Yang et al., 2020] constructs multi-stage curriculum learning to address the difficulty of multi-agent exploration while focusing on single-stage tasks with multiple parallel goals. CMAE [Liu et al., 2021] trains exploration policies to reach a shared goal state selected from restricted spaces. Some approaches [Mahajan et al., 2019; Mahajan et al., 2019; Li et al., 2021a] have been proposed recently to encourage extensive exploration. In this paper, we focus on multistage tasks with sequential goals. By considering consistency at the goal level to guide efficient exploration, our method improves sample efficiency and promotes multi-agent cooperation in multistage tasks. ## 3 Preliminaries In this section, we formalize the multistage MARL problem as a multistage partially observable Markov game. A multiagent learning problem that contains L stages is defined as a tuple 〈S,A,O,R,G,Z, P, γ〉, where S is the set of environment state s, and G is the set of goals to achieve. At each stage l ∈ [1 · · · L], agents learn to finish sub-goals gl ∈ G. O = [O1, · · · ,ON] is a finite set of joint observations. Each agent i ∈ [1 · · ·N] observes oi ∈ Oi drawn from the observation function Z : S ×A → O. Then, agent i ∈ {1 · · ·N} chooses its own action ai ∈ Ai, forming a joint action a ∈ A, which induces state transition according to the state transition function P (s′ |s, a) : S × A × S → [0, 1]. After that, each agent i receives a reward ri ∈ Ri : S ×A×G → R. Without loss of generality, all joint quantities over agents are denoted in bold. The objective for each agent is to learn a policy πi(ai|oi, gl) : Oi×Ai×G → [0, 1] to maximize the expected cumulative reward received E[∑Ht=0 γ t rt i ] over horizonH, in which γ ∈ [0, 1) is a discount factor. ## 4 Methodology In this section, we specifically introduce our multi-agent goal consistency (MAGIC) framework to facilitate cooperation in multistage tasks. First, inspired by the human decisionmaking process from goal cognition to action, we explicitly incorporate goal cognition into the learning process of agents, and design a goal-orientated actor-critic model, which learns two views of goal cognition. Second, we formally introduce two-level goal consistency training to drive agents to have consistent goal cognition, thus avoiding useless exploration and promoting cooperation. ## 4.1 Goal-oriented Actor-Critic Model In complex multistage environments, it is difficult to learn useful strategies through aimless exploration. To motivate effective exploration, we propose a goal-orientated actor-critic model, by which agents learn to act and criticize based on the learned goal cognition. Moreover, taking advantage of centralized training and decentralized execution, the decentralized policies (actors) and the centralized critics learn the local and global views of goal cognition, respectively. ## Goal Cognition in Local View To derive goal-oriented policies for effective exploration, we propose a general goal cognition module (GCM) to learn the local view of goal cognition adaptively from local observations. For agent i, the observation oi can be expressed as oi = {oi-g , oig1 , · · · , oigT }, where oigj is the information about the goal gj observed by agent i, called goal-specific Figure 1: The goal-oriented policy network architecture of agent i. observation. oi-g is the rest of oi . Since oi-g contains all information about the task and environment except for the goal entities, we exploit oi-g to cognitive goals in decentralized policy networks. As shown in Fig.1, we first encode the local observation to get feature representations by embedding functions, where oi-g and oigj are encoded into feature vector e-g and ej respectively. Then, the feature representation vectors are passed into the goal cognition module based on the soft-attention mechanism to recognize which goal is more important for the current stage. Specifically, the goal cognition module compares the embedding e-g with ej and passes the matching value between these two embeddings into a softmax function: ![Image](https://ijcai.org/proceedings/2022/fulltext/25/image_000000_4300ca2207dd6bbd3b776833b8080a3de5b57f29205eb8c182089c4f3665f30b.png) where wi,j denotes the importance of goal gj for agent i. The importance distribution of the goal-specific observations W i local exactly represents goal cognition of agent i in local view. A uniform distribution means that the agent has no clear cognition of the current goal, and it may act with equal consideration of all goals. The goal gj with the greatest importance is seen as the agent's selected goal. Through goal cognition, agents pay more attention to the information of the selected goal, and take actions based on the feature vector e-g and the weighted sum of each goal-specific observation: where fi is a fully connected layer. In this way, agents learn to dynamically focus on the observable goal information in each stage and act based on their local view of goal cognition. Novelty of GCM. GCM empowers agents with the ability to explicitly cognitive stage goals by adopting a simple attention kernel. Unlike existing algorithms that learn the relationship between agents with attention mechanisms, GCM focuses on the importance of each goal in the current state, which is conducive to discovering the goal sequence of multistage tasks and facilitating targeted exploration. ## Goal Cognition in Global View During the centralized training, utilizing the information of all agents, the critics learn the global view of goal cognition and evaluate the value of observation-action pairs. Figure 2: Training agents with two-level goal consistency by using the self cognitive dissonance loss (TCD-loss) and the team cognitive dissonance loss (SCD-loss). ![Image](https://ijcai.org/proceedings/2022/fulltext/25/image_000001_2b882baa80a494d941a86933eb5720cf7f75ce82c760f0eff50a52ed7e6dde3c.png) Similar to the architecture of policy networks, the critic Qi for agent i derives goal-specific observation from oi . Combined with the information of other agents, we obtain global non-goal feature representation e-g global = fi(oi-g , o-i , a), where o-i denotes observations except agent i and a is the joint action. The attention weight wi,j global of goal j can be represented as: where ejglobal = fi(oigj ) is the feature representation of goal j. Then, the critic parameterized by ωi of agent i evaluates actions more accurately based on goal cognition in the global view W i global : ## 4.2 Training with Goal Consistency Without a consistent goal, it is difficult to quickly emerge cooperative behavior from the learned goal cognition with limited knowledge. Therefore, we propose to train agents with two-level goal consistency, driving consistent goal cognition for effective exploration. First, we align the local view of goal cognition with the global view of goal cognition, which guides goal cognition learning in a self-supervised way. Second, we align the goal cognition of each agent with their teammates to foster a collective consistent stage-goal. ## Consistency through Self-supervised Learning Assuming that at time step t, the true goal Gt depends on the global state st represented as Gt = p(Gt | st), where p(Gt | st) is the distribution of the true goal. To attain goal consistency, the goal gi t,local selected by agent i in local view based on its local observation oit should be similar to the true goal Gt. Analogous to the consistency constraint using KL divergence in [Mao et al., 2020], we achieve self-supervised learning by minimizing the following object: Nevertheless, agents are not able to know the true distribution of goal in advance. To solve this problem, we suppose that the selected goal in a global view based on the joint observation ot = {o1t , · · · , oNt } and the joint action at = {a1t , · · · , aNt } of all agents will act as a precise approximation of the true stage-goal. Therefore, we replace the true goal Gt with the goal distribution gi t,global attained in the critics to supervise the learning of goal cognition for the actors, and propose the self cognitive dissonance loss (SCD-loss) as: In such self-supervised way, the actors will continuously learn a better goal cognition. ## Consistency through Aligning Teammates Intuitively, if agents have similar goal cognition, they are more likely to reach the same goal. As mentioned before, each agent learns goal cognition in both the local view and global view, during which agents should keep consistent cognition with their teammates. We achieve this by aligning the goal cognition distribution with that of other agents. Specifically, for agent i, we minimize the team cognitive dissonance loss (TCD-loss) in a local view: Similarly, the consistent global goal cognition is achieved by minimizing the team cognitive dissonance loss as Novelty of two-level goal consistency training. The twolevel goal consistency training is rooted in goal cognition and encourages consistent exploration behaviors. Unlike the latest NCC-Q [Mao et al., 2020] that learns aimless neighborhood cognition from low-level observations, goal consistency learning directly guides efficient exploration for agents at the goal level. ## Training of MAGIC MAGIC adopts an actor-critic architecture and can be easily combined with existing CTDE-based methods, such as MADDPG and Mean Field. Here, we illustrate the training process of MAGIC based on MADDPG, where the key is the two-level goal consistency training for driving cooperation. As shown in Fig.2, the two-level goal consistency training uses the team cognitive dissonance loss (TCD-loss) to constrain agents to be consistent with other agents in both local and global views of goal cognition, and exploits the selfcognitive dissonance loss (SCD-loss) to supervise the learning of goal cognition in local view. Specifically, the critic Qi (o, a; ωi) is trained by minimizing the combination of the temporal-difference loss (TD-loss) and the team cognitive dissonance loss (TCD-loss) in global view as follows: As for the actors of MAGIC, it is trained not only by the policy gradient but also the two-level goal consistency loss. First, we can write the gradient of the expected return for agent i as: Then two-level goal consistency loss function of J tcd (θi ) and J scd (θi ) is Finally, the actor is updated by: ## 5 Experiments In this section, we evaluate the effectiveness of MAGIC in three multistage cooperative tasks: resource collection, multipoint transport and cooperative endangered wildlife rescue. ## 5.1 Settings ## Baselines We compare MAGIC with independent DDPG [Lillicrap et al., 2016], MADDPG [Lowe et al., 2017], Mean Field [Yang et al., 2018], MAAC [Iqbal and Sha, 2019], NCC-Q [Mao et al., 2020] and GA-AC [Liu et al., 2020]. MAAC focuses on information of the other agents dynamically by using the soft-attention (SA) mechanism. As we have found that DDPG shows better performance in our experiments than the original version using SAC, MAAC is implemented as DDPG+SA with experience sharing for a fair comparison. Mean Field MARL learns by approximating the interactions within agents with an average effect. NCC-Q learns neighborhood cognitive consistency to facilitate agent cooperation. GA-AC learns the relationship between agents by using game abstraction mechanism based on a two-stage attention network. In addition, we add sample efficient IGASIL [Hao et al., 2019] as a baseline for the sparse reward task of endangered wildlife rescue. ## Training Details For scenarios of resource collection and endangered wildlife rescue, we set α = 1, λ = 0.001 and β = 1. For the multipoint transportation task, we set α = λ = 0.1 and β = 0.01. ## 5.2 Resource Collection ## Game Settings The resource collection task requires agents to collect resources in multiple resource pools in sequence. There are N agents and L resource pools in a randomly generated environment, as shown in Fig.3(a). At different stage, one resource pool can be developed. The next target resource pool is accessible only when agents successfully mines the current target resource pool collaboratively. Therefore, agents should learn to recognize the goal of each stage and tap a resource pool cooperatively. Each agent receives a dense reward from the environment at each step, which are related to distance to the goals. We evaluate our method in the cases of (N = 4, L = 3) and (N = 10, L = 3). Figure 3: Resource collection: (a) Task description; (b)(c)The number of goals finished in the case of (N=4,L=3) and (N=10,L=3); (d) The average goal cognition values (attention weights) of agents at each stage when required to complete the goals [g1, g2, g3] in order. ![Image](https://ijcai.org/proceedings/2022/fulltext/25/image_000002_55d5818cba462d5fb6e0d616fcba63c999dad8ea3ea52280983bb151e594689f.png) ## Results The results are shown in Fig.3(b) and Fig.3(c), where we evaluate the number of goals finished. Obviously, learning with shaped rewards reduces the difficulty of exploration for all methods, MAGIC outperforms other baselines in accelerating the learning process and improving the policy performance. Moreover, in Fig.3(c), as the input dimension increases exponentially with the number of agents, MAGIC still learns faster than the other methods with higher performance while the performance of MADDPG decreases sharply, indicating that goal consistency promotes the efficient exploration and cooperation of large-scale agents. The other baselines that focus on neighborhood-level coordination perform poorly in global objective optimization, even worse than MADDPG that learns global-level coordination by naively considers information of all agents. NCC-Q is found ineffective to solve complex multistage tasks, since agents hardly benefit from the aimless neighborhood cognitive consistency, which even introduces complex network training process. The poor performance of GA-AC and MAAC also implies that making much effort on learning relationship among agents hinders efficient exploration of goals in multistage tasks. To further analyze whether agents learn to cognitive goals of different stages and maintain consistent goals, we show the average goal cognition values (attention weights) of all agents in Fig.3(d), which describes the changes of average attention weights over three stages in the task that requires to complete goals in the sequence of [g1, g2, g3]. We observe that the goal with the highest average weight at each stage corresponds to the actual goal, indicating that agents have good goal cognition. Moreover, the weights of the selected goals are clearly distinguished from that of other goals, which means that the agents have formed a consistent goal cognition. ## 5.3 Multi-Point Transportation ## Game Settings The multi-point transportation task requires N agents to cooperatively push a big ball to L destinations in order, as shown in the Fig.4(left). This task is similar to the realworld cargo transportation task that require the cargo to pass through multiple delivery points in sequence according to the prescribed routes. Dense rewards are offered to agents in each step. We evaluate our method in the case of (N = 4, L = 3). ## Results The multi-point transportation task is more difficult than the resource collection task, due to the requirement for pushing a ball cooperatively and navigating to multiple points. In Fig.4(right), we show the learning curves of the number of goals finished by different algorithms. MAGIC learns faster and converges to a much higher goal completion rate than all the baselines. Without a consistent goal, agents trained by other methods may push the ball in different directions, thus wasting a lot of exploration time. However, agents trained by MAGIC learn to quickly transfer the moving direction to the next goal when they finish the current stage-goal. Moreover, when reaching the last destination, some agents will turn to the opposite direction of the ball to slow it down. It is verified that agents have benefited from goal consistency training, which greatly accelerates cooperation among agents and promotes effective exploration. Figure 4: Multi-point transportation: (Left) Task description. (Right) The number of goals finished for MAGIC and the baselines. ![Image](https://ijcai.org/proceedings/2022/fulltext/25/image_000003_5af16a21b6cec42c36079e86db004257f7f6471ca8293e93474b2c6141da1066.png) ## 5.4 EndangeredWildlife Rescue ## Game Settings The endangered wildlife rescue is a sparse reward task modified from the predator-prey scenario in [Lowe et al., 2017]. As shown in the Fig.5(left), there are N = 2 slowly rescue agents and M = 2 faster wounded animals. One of the animal g1 is seriously injured (in red) which should be rescued first, and the other animal g2 in pink is less injured. The target for each rescue agent is to rescue all wounded animals according to the severity of the injury. Agents are rewarded only if they save an animal simultaneously, and punished for miss-coordination. Different wounded animals correspond to different rewards and different risks (penalties). The sparse reward setting poses significant challenges to exploration and policy learning. ## Results To make different algorithms comparable, we pre-train both the rescue agents and wounded animal agents with DDPG and save the animal models during training. Then, we reuse the same pre-trained animal models as the default policies for the wounded animals in all experiments, thus the environment is fully cooperative. Figure 5: Endangered wildlife rescue: (Left) Task description. (Right) The number of goals finished for MAGIC and other five baselines. ![Image](https://ijcai.org/proceedings/2022/fulltext/25/image_000004_792e6948e8adf2a16698c2cdffb5db6a8157e26bd527c7b660fb33e4fd385bb4.png) As can be seen in Fig.5(right), MAGIC significantly surpasses all baselines in convergence speed and final performance. In this difficult sparse reward task, MADDPG and Mean Field perform poorly in learning efficiency and result in sub-optimal policies. IGASIL also fails to achieve continually cooperative by aimlessly self-imitation learning. While these baseline methods hardly learn to rescue animals cooperatively by aimless policy learning, the advantage of MAGIC is more prominent in the multistage task with sparse reward setting, where consensus is extremely critical due to the lack of effective guiding signals. ## 5.5 Discussion ## Ablation Study In this section, we further verify how goal consistency affects the learning process in the endangered wildlife rescue task. We implement three ablation variants of MAGIC: (1) MAGIC w/o TCD-loss removes TCD-loss during training, (2) MAGIC w/o SCD-loss removes SCD-loss, (3) GON removes both TCD-loss and SCD-loss. As shown in Fig.6(left), removing TCD-loss (MAGIC w/o TCD-loss) results in lower learning efficiency than removing SCD-loss (MAGIC w/o SCD-loss), which means that aligning with teammates plays a more important role in driving goal consistency than self-supervised learning. MAGIC significantly outperforms GON in final goal completion rate by exploiting the two-level goal consistency training. Although GON fails to coordinate to rescue both wounded animals, it performs better than all the baseline methods shown in the Fig.5(right), indicating that learning goal cognition before making a decision provides effective guidance for agent training. More importantly, the superiority of MAGIC demonstrates that the two-level goal consistency loss further accelerates goal-oriented exploration process of agents. ## Universality Study To study the universality of MAGIC, we increase the coordination difficulty and the number of agents in the resource collection task to investigate the scope of solvable problems by using the MAGIC framework. On the one hand, considering the coordination requirements of the actual task, we build a two-stage resource collection task with two resource pools in the second phase. Therefore, the optimal strategy of agents in the second stage is to divide into two groups coordinately. As shown in Fig.6(right), MAGIC still achieves higher sample efficiency compared with other algorithms, which implies that MAGIC can effectively accelerate the learning process of cooperative tasks, even in tasks with coordination difficulty. We also observe that all algorithms converge to suboptimal strategies, where MAGIC agents tend to act concertedly under the constraints of goal consistency. To study how to divide groups and impose goal consistency constraints among agents in the same group is considerable future work. Figure 6: (Left) Ablation results in the endangered wildlife rescue task. (Right) Results in a two-stage resource collection task with two resource pools in the second phase. ![Image](https://ijcai.org/proceedings/2022/fulltext/25/image_000005_bf857f7e6bdf9f4615f64ec3f384e4a181b8b5202d146f83e7b4548398907a7c.png) Figure 7: Results of resource collection tasks (L = 3) with largescale agents. (a) The episode rewards for MAGIC-MF and Mean Filed algorithm when N = 20 ; (b) Percentage of sample efficiency that MAGIC-MF improves compared to Mean Field algorithm when N = [5, 10, 20]. Note that parameter sharing trick is used to ease training in the universality study experiments. ![Image](https://ijcai.org/proceedings/2022/fulltext/25/image_000006_acfe6709a3930820f84211b750241c0e232f675889463c1871ac32a1dc5b2d96.png) On the other hand, considering complex scenarios of largescale agents, we combine MAGIC with Mean Field algorithm to form MAGIC-MF and evaluate it in the case of N = [5, 10, 20]. Although the Mean Field algorithm has been proved to be capable of handling large-scale agents in singlestage tasks, its aimless exploration in multi-stage tasks leads to difficulty in finding stage goals. However, MAGIC-MF significantly improves sample efficiency, especially in largescale agents scenarios (as shown in Fig.7). It suggests that MAGIC can be easily combined with existing advanced algorithms to accelerate learning in a variety of complex multistage cooperative tasks. ## 6 Conclusions In this paper, we focus on multistage cooperative tasks with sequential goals and propose multi-agent goal consistency (MAGIC) framework, which improves goal cognition ability for agents and achieves effective exploration. MAGIC learns goal cognition by using a goal-oriented actor-critic model, which enables agents to focus on different goals in different stages adaptively . Moreover, MAGIC further facilitates exploration efficiency by introducing tow-level goal consistency. Empirically results demonstrate that MAGIC improves sample efficiency in challenging multistage tasks universally, especially in tasks with sparse reward and large-scale agents. ## Acknowledgments This work is partially supported by the National Key R&D Program of China (Grant No.2020YFB2104000), the National Natural Science Foundation of China (Grant Nos. 62172154, 61772559), the Hunan Provincial Natural Science Foundation of China under grant No.2020JJ3016. Dr. Xuan Liu is partially supported by the National Defense Science and Technology Innovation Special Zone Project of China. Xuan Liu is the corresponding author of this paper. ## References 1. [Ding et al., 2020] Ziluo Ding, Tiejun Huang, and Zongqing Lu. Learning individually inferred communication for multi-agent cooperation. In Proceedings of Conference on Neural Information Processing Systems, 2020. 2. [Gronauer and Diepold, 2021] S. Gronauer and K. Diepold. Multi-agent deep reinforcement learning: a survey. 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