Learning agents that are not only capable of taking tests but also innovating is becoming the next hot topic in AI. One of the most promising paths towards this vision is multi-agent learning, where agents act as the environment for each other, and improving each agent means proposing new problems for the others. However, existing evaluation platforms are either not compatible with multi-agent settings, or limited to a specific game. That is, there is not yet a general evaluation platform for research on multi-agent intelligence. To this end, we introduce Arena, a general evaluation platform for multi-agent intelligence with games of diverse logic and representations. Furthermore, multi-agent intelligence is still at the stage where many problems remain unexplored. Thus, we provide a building toolkit for researchers to invent and build novel multi-agent problems from the provided game set with little efforts. Finally, we provide python implementations of five state-of-the-art deep multi-agent reinforcement learning baselines. Along with the baseline implementations, we release a set of 100 best agents teams that we can train with different training schemes for each game, as the base for evaluating agents with population performance, so that the research community can perform comparisons under a stable and uniform standard.
Surveys of multi-agent intelligence research can be found here @cite_72 @cite_58 @cite_109 @cite_14 @cite_65 . Different ideas have been explored on competitive and collaborative multi-agent settings, respectively.
@cite_14 @cite_65 @cite_109 @cite_72 @cite_58
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In the framework of fully cooperative multi-agent systems, independent (non-communicative) agents that learn by reinforcement must overcome several difficulties to manage to coordinate. This paper identifies several challenges responsible for the non-coordination of independent agents: Pareto-selection, non-stationarity, stochasticity, alter-exploration and shadowed equilibria. A selection of multi-agent domains is classified according to those challenges: matrix games, Boutilier's coordination game, predators pursuit domains and a special multi-state game. Moreover, the performance of a range of algorithms for independent reinforcement learners is evaluated empirically. Those algorithms are Q-learning variants: decentralized Q-learning, distributed Q-learning, hysteretic Q-learning, recursive frequency maximum Q-value and win-or-learn fast policy hill climbing. An overview of the learning algorithms' strengths and weaknesses against each challenge concludes the paper and can serve as a basis for choosing the appropriate algorithm for a new domain. Furthermore, the distilled challenges may assist in the design of new learning algorithms that overcome these problems and achieve higher performance in multi-agent applications. Artificial Intelligence has become one of the most fundamental areas of computer science research. One line of research in artificial intelligence is associated with coordination of intelligent agents. Coordination takes place in multi-agent environments where distributed agents have limited knowledge about their surrounding environment; therefore, they continuously ask other agents to obtain required information. In general, the growing popularity of agents requires systematic coordination management and reputation system that enable agents to decide about their interacting partner and overall acting attitude. Using the reputation system, agents choose reliable agents to interact with. Here, the question arises that how the reputation mechanism helps agents to make the most prudent decisions that yield the best outcome. More specifically, agents need to use a decision-theoretic reasoning algorithm to optimize their decisions under uncertainty. In multi-agent systems, the reputation value is highly competitive because it is used as the beliefs of agents about one another. Thus, a carefully designed mechanism is required to maintain accuracy of this parameter. Many artificial intelligence (AI) applications often require multiple intelligent agents to work in a collaborative effort. Efficient learning for intra-agent communication and coordination is an indispensable step towards general AI. In this paper, we take StarCraft combat game as a case study, where the task is to coordinate multiple agents as a team to defeat their enemies. To maintain a scalable yet effective communication protocol, we introduce a Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a vectorised extension of actor-critic formulation. We show that BiCNet can handle different types of combats with arbitrary numbers of AI agents for both sides. Our analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of advanced coordination strategies that have been commonly used by experienced game players. In our experiments, we evaluate our approach against multiple baselines under different scenarios; it shows state-of-the-art performance, and possesses potential values for large-scale real-world applications. Communicating and sharing intelligence among agents is an important facet of achieving Artificial General Intelligence. As a first step towards this challenge, we introduce a novel framework for image generation: Message Passing Multi-Agent Generative Adversarial Networks (MPM GANs). While GANs have recently been shown to be very effective for image generation and other tasks, these networks have been limited to mostly single generator-discriminator networks. We show that we can obtain multi-agent GANs that communicate through message passing to achieve better image generation. The objectives of the individual agents in this framework are two fold: a co-operation objective and a competing objective. The co-operation objective ensures that the message sharing mechanism guides the other generator to generate better than itself while the competing objective encourages each generator to generate better than its counterpart. We analyze and visualize the messages that these GANs share among themselves in various scenarios. We quantitatively show that the message sharing formulation serves as a regularizer for the adversarial training. Qualitatively, we show that the different generators capture different traits of the underlying data distribution.