In this paper, we propose Unreal Multi-Agent Playground (Unreal-MAP), an MARL general platform based on the Unreal-Engine (UE). Unreal-MAP allows users to freely create multi-agent tasks using the vast visual and physical resources available in the UE community, and deploy state-of-the-art (SOTA) MARL algorithms within them. Unreal-MAP is user-friendly in terms of deployment, modification, and visualization, and all its components are open-source. We also develop an experimental framework compatible with algorithms ranging from rule-based to learning-based provided by third-party frameworks. Lastly, we deploy several SOTA algorithms in example tasks developed via Unreal-MAP, and conduct corresponding experimental analyses including a sim2real demo. We believe Unreal-MAP can play an important role in the MARL field by closely integrating existing algorithms with user-customized tasks, thus advancing the field of MARL.

State-of-the-art (SOTA) multiagent reinforcement algorithms distinguish themselves in many ways from their single-agent equivalences. However, most of them still totally inherit the single-agent exploration-exploitation strategy. Naively inheriting this strategy from single-agent algorithms causes potential collaboration failures, in which the agents blindly follow mainstream behaviors and reject taking minority responsibility. We name this problem the responsibility diffusion (RD) as it shares similarities with the same-name social psychology effect. In this work, we start by theoretically analyzing the cause of this RD problem, which can be traced back to the exploration-exploitation dilemma of multiagent systems (especially large-scale multiagent systems). We address this RD problem by proposing a policy resonance (PR) approach which modifies the collaborative exploration strategy of agents by refactoring the joint agent policy while keeping individual policies approximately invariant. Next, we show that SOTA algorithms can equip this approach to promote the collaborative performance of agents in complex cooperative tasks. Experiments are performed in multiple test benchmark tasks to illustrate the effectiveness of this approach.

A large proportion of recent studies on cooperative Multi-Agent Reinforcement Learning (MARL) focus on the policylearning process in scenarios with stationary opponents (or without opponents). This paper, instead, investigates a different challenge of achieving team superiority in dynamic competitions among competitors that evolve dynamically with MARL. We aim to enhance the competitiveness of such MARL learners by enabling them to adjust their own learning settings dynamically, so as to take quick counter-measures against the policy shift of competitor learners, or to learn faster to suppress the opponents. We propose a Competitive Auto-Multiagent Learner with Fuzzy Feedback (CALF) with two essential highlights: (1) CALF establishes feedback controllers to achieve real-time adjustments based on fuzzy logic, using human-readable fuzzy rules to provide significant explainability and flexibility; (2) CALF integrates Bayesian Optimization to search and optimize the feedback fuzzy logic rules automatically. CALF can be used to apply real-time adjustments for MARL hyperparameters and intrinsic rewards. We also give solid empirical results to show that CALF significantly promotes team competitiveness in adversarial competitions, spanning from small-scale tasks involving 2 teams to large-scale tasks involving 3 teams and hundreds of agents. Furthermore, CALF exhibits superior competitiveness when engaging in competition with established competitors like Qmix, Qtran, and Qplex in dynamic competitive environments. Moreover, the experiments also demonstrate that the integration of the fuzzy logic with Bayesian Optimization offers considerable transferability and explainability, enabling a CALF-implemented learner optimized from one scenario to be transferred to other distinct scenarios.

Multi-agent Reinforcement Learning (MARL) has become a powerful tool for addressing multi-agent challenges. Existing studies have explored numerous models to use MARL to solve single-team cooperation (competition) problems and adversarial problems with opponents controlled by static knowledge-based policies. However, most studies in the literature often ignore adversarial multi-team problems involving dynamically evolving opponents. We investigate adversarial multi-team problems where all participating teams use MARL learners to learn policies against each other. Two objectives are achieved in this study. Firstly, we design an adversarial team-versus-team learning framework to generate cooperative multi-agent policies to compete against opponents without preprogrammed opponent partners or any supervision. Secondly, we explore the key factors to achieve win-rate superiority during dynamic competitions. Then we put forward a novel FeedBack MARL (FBMARL) algorithm that takes advantage of feedback loops to adjust optimizer hyper-parameters based on real-time game statistics. Finally, the effectiveness of our FBMARL model is tested in a benchmark environment named Multi-Team Decentralized Collective Assault (MT-DCA). The results demonstrate that our feedback MARL model can achieve superior performance over baseline competitor MARL learners in 2-team and 3-team dynamic competitions.

When dealing with a series of imminent issues, humans can naturally concentrate on a subset of these concerning issues by prioritizing them according to their contributions to motivational indices, e.g., the probability of winning a game. This idea of concentration offers insights into reinforcement learning of sophisticated Large-scale Multi-Agent Systems (LMAS) participated by hundreds of agents. In such an LMAS, each agent receives a long series of entity observations at each step, which can overwhelm existing aggregation networks such as graph attention networks and cause inefficiency. In this paper, we propose a concentration network called ConcNet. First, ConcNet scores the observed entities considering several motivational indices, e.g., expected survival time and state value of the agents, and then ranks, prunes, and aggregates the encodings of observed entities to extract features. Second, distinct from the well-known attention mechanism, ConcNet has a unique motivational subnetwork to explicitly consider the motivational indices when scoring the observed entities. Furthermore, we present a concentration policy gradient architecture that can learn effective policies in LMAS from scratch. Extensive experiments demonstrate that the presented architecture has excellent scalability and flexibility, and significantly outperforms existing methods on LMAS benchmarks.

Multiagent reinforcement learning (MARL) can solve complex cooperative tasks. However, the efficiency of existing MARL methods relies heavily on well-defined reward functions. Multiagent tasks with sparse reward feedback are especially challenging not only because of the credit distribution problem, but also due to the low probability of obtaining positive reward feedback. In this paper, we design a graph network called Cooperation Graph (CG). The Cooperation Graph is the combination of two simple bipartite graphs, namely, the Agent Clustering subgraph (ACG) and the Cluster Designating subgraph (CDG). Next, based on this novel graph structure, we propose a Cooperation Graph Multiagent Reinforcement Learning (CG-MARL) algorithm, which can efficiently deal with the sparse reward problem in multiagent tasks. In CG-MARL, agents are directly controlled by the Cooperation Graph. And a policy neural network is trained to manipulate this Cooperation Graph, guiding agents to achieve cooperation in an implicit way. This hierarchical feature of CG-MARL provides space for customized cluster-actions, an extensible interface for introducing fundamental cooperation knowledge. In experiments, CG-MARL shows state-of-the-art performance in sparse reward multiagent benchmarks, including the anti-invasion interception task and the multi-cargo delivery task.

Low‐light images suffer from severe noise and low illumination. In this work, the authors propose an adaptive low‐light raw image enhancement network to avoid parameter‐handcrafting in current deep learning models and to improve image quality. The proposed method can be divided into two sub‐models: brightness prediction and exposure shifting (ES). The former is designed to control the brightness of the resulting image by estimating a guideline exposure time . The latter learns to approximate an exposure‐shifting operator ES, converting a low‐light image with real exposure time to a noise‐free image with guideline exposure time . Additionally, structural similarity loss and image enhancement vector are introduced to promote image quality, and a new campus image data set (CID) for training the proposed model is proposed to overcome the limitations of the existing data sets. In quantitative tests, it is shown that the proposed method has the lowest noise level estimation score compared with the state‐of‐the‐art low‐light algorithms, suggesting a superior denoising performance. Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene. Lastly, the potential application in video processing is briefly discussed.