In logics for the strategic reasoning the main challenge is represented by their verification in contexts of imperfect information and perfect recall. In this work, we show a technique to approximate the verification of Alternating-time Temporal Logic (ATL*) under imperfect information and perfect recall, which is known to be undecidable. Given a model M and a formula $\varphi$, we propose a verification procedure that generates sub-models of M in which each sub-model M' satisfies a sub-formula $\varphi'$ of $\varphi$ and the verification of $\varphi'$ in M' is decidable. Then, we use CTL* model checking to provide a verification result of $\varphi$ on M. We prove that our procedure is in the same class of complexity of ATL* model checking under perfect information and perfect recall, we present a tool that implements our procedure, and provide experimental results.

Reinforcement learning (RL) has achieved striking empirical successes in solving real-world sequential decision-making problems (Mnih et al., 2015; Duan et al., 2016; Silver et al., 2016, 2017, 2018; Agostinelli et al., 2019; Akkaya et al., 2019). Motivated by these successes, multi-agent extensions of RL algorithms have recently become popular in decision-making problems involving multiple interacting agents (Busoniu et al., 2008; Hernandez-Leal et al., 2018, 2019; OroojlooyJadid and Hajinezhad, 2019; Zhang et al., 2019). Multi-agent RL is often modeled as a Markov game (Littman, 1994) where, at each time step, given the state of the environment, each player (agent) takes an action simultaneously, observes her own immediate reward, and the environment evolves into a next state. Here both the reward of each player and the state transition depend on the actions of all players. From the perspective of each player, her goal is to find a policy that maximizes her expected total reward in the presence of other agents. In Markov games, depending on the structure of the reward functions, the relationship among the players can be either collaborative, where each player has the same reward function, or competitive, where the sum of the reward function is equal to zero, or mixed, which corresponds to a general-sum game. While most of the existing theoretical results focus on the collaborative or two-player competitive settings, the mixed setting is oftentimes more pertinent to real-world multi-agent applications. Moreover, in addition to having diverse reward functions, the players might also have asymmetric roles in the Markov game--the players might be divided into leaders and followers, where the leaders' joint policy determines a general-sum game for the followers.

Deep reinforcement learning (RL) has recently shown great promise in robotic continuous control tasks. Nevertheless, prior research in this vein center around the centralized learning setting that largely relies on the communication availability among all the components of a robot. However, agents in the real world often operate in a decentralised fashion without communication due to latency requirements, limited power budgets and safety concerns. By formulating robotic components as a system of decentralised agents, this work presents a decentralised multiagent reinforcement learning framework for continuous control. To this end, we first develop a cooperative multiagent PPO framework that allows for centralized optimisation during training and decentralised operation during execution. However, the system only receives a global reward signal which is not attributed towards each agent. To address this challenge, we further propose a generic game-theoretic credit assignment framework which computes agent-specific reward signals. Last but not least, we also incorporate a model-based RL module into our credit assignment framework, which leads to significant improvement in sample efficiency. We demonstrate the effectiveness of our framework on experimental results on Mujoco locomotion control tasks. For a demo video please visit: https://youtu.be/gFyVPm4svEY.

A swarm intelligence-based optimization algorithm, named Duck Swarm Algorithm (DSA), is proposed in this paper. This algorithm is inspired by the searching for food sources and foraging behaviors of the duck swarm. The performance of DSA is verified by using eighteen benchmark functions, where it is statistical (best, mean, standard deviation, and average running time) results are compared with seven well-known algorithms like Particle swarm optimization (PSO), Firefly algorithm (FA), Chicken swarm optimization (CSO), Grey wolf optimizer (GWO), Sine cosine algorithm (SCA), and Marine-predators algorithm (MPA), and Archimedes optimization algorithm (AOA). Moreover, the Wilcoxon rank-sum test, Friedman test, and convergence curves of the comparison results are used to prove the superiority of the DSA against other algorithms. The results demonstrate that DSA is a high-performance optimization method in terms of convergence speed and exploration-exploitation balance for solving high-dimension optimization functions. Also, DSA is applied for the optimal design of two constrained engineering problems (the Three-bar truss problem, and the Sawmill operation problem). Additionally, four engineering constraint problems have also been used to analyze the performance of the proposed DSA. Overall, the comparison results revealed that the DSA is a promising and very competitive algorithm for solving different optimization problems.

Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reinforces the fact that such customer-centric design of these services may lead to unfair distribution of exposure to the producers, which may adversely impact their well-being. On the other hand, a pure producer-centric design might become unfair to the customers. As more and more people are depending on such platforms to earn a living, it is important to ensure fairness to both producers and customers. In this work, by mapping a fair personalized recommendation problem to a constrained version of the problem of fairly allocating indivisible goods, we propose to provide fairness guarantees for both sides. Formally, our proposed {\em FairRec} algorithm guarantees Maxi-Min Share ($\alpha$-MMS) of exposure for the producers, and Envy-Free up to One Item (EF1) fairness for the customers. Extensive evaluations over multiple real-world datasets show the effectiveness of {\em FairRec} in ensuring two-sided fairness while incurring a marginal loss in overall recommendation quality. Finally, we present a modification of FairRec (named as FairRecPlus) that at the cost of additional computation time, improves the recommendation performance for the customers, while maintaining the same fairness guarantees.

The field of artificial intelligence (AI) is devoted to the creation of artificial decision-makers that can perform (at least) on par with the human counterparts on a domain of interest. Unlike the agents in traditional AI, the agents in artificial general intelligence (AGI) are required to replicate human intelligence in almost every domain of interest. Moreover, an AGI agent should be able to achieve this without (virtually any) further changes, retraining, or fine-tuning of the parameters. The real world is non-stationary, non-ergodic, and non-Markovian: we, humans, can neither revisit our past nor are the most recent observations sufficient statistics. Yet, we excel at a variety of complex tasks. Many of these tasks require longterm planning. We can associate this success to our natural faculty to abstract away task-irrelevant information from our overwhelming sensory experience. We make task-specific mental models of the world without much effort. Due to this ability to abstract, we can plan on a significantly compact representation of a task without much loss of performance. Not only this, we also abstract our actions to produce high-level plans: the level of action-abstraction can be anywhere between small muscle movements to a mental notion of "doing an action". It is natural to assume that any AGI agent competing with humans (at every plausible domain) should also have these abilities to abstract its experiences and actions. This thesis is an inquiry into the existence of such abstractions which aid efficient planing for a wide range of domains, and most importantly, these abstractions come with some optimality guarantees.

One of the preeminent obstacles to scaling multi-agent reinforcement learning to large numbers of agents is assigning credit to individual agents' actions. In this paper, we address this credit assignment problem with an approach that we call \textit{partial reward decoupling} (PRD), which attempts to decompose large cooperative multi-agent RL problems into decoupled subproblems involving subsets of agents, thereby simplifying credit assignment. We empirically demonstrate that decomposing the RL problem using PRD in an actor-critic algorithm results in lower variance policy gradient estimates, which improves data efficiency, learning stability, and asymptotic performance across a wide array of multi-agent RL tasks, compared to various other actor-critic approaches. Additionally, we relate our approach to counterfactual multi-agent policy gradient (COMA), a state-of-the-art MARL algorithm, and empirically show that our approach outperforms COMA by making better use of information in agents' reward streams, and by enabling recent advances in advantage estimation to be used.

Multi-agent reinforcement learning (MARL) enables us to create adaptive agents in challenging environments, even when the agents have limited observation. Modern MARL methods have hitherto focused on finding factorized value functions. While this approach has proven successful, the resulting methods have convoluted network structures. We take a radically different approach, and build on the structure of independent Q-learners. Inspired by influence-based abstraction, we start from the observation that compact representations of the observation-action histories can be sufficient to learn close to optimal decentralized policies. Combining this observation with a dueling architecture, our algorithm, LAN, represents these policies as separate individual advantage functions w.r.t. a centralized critic. These local advantage networks condition only on a single agent's local observation-action history. The centralized value function conditions on the agents' representations as well as the full state of the environment. The value function, which is cast aside before execution, serves as a stabilizer that coordinates the learning and to formulate DQN targets during learning. In contrast with other methods, this enables LAN to keep the number of network parameters of its centralized network independent in the number of agents, without imposing additional constraints like monotonic value functions. When evaluated on the StarCraft multi-agent challenge benchmark, LAN shows state-of-the-art performance and scores more than 80% wins in two previously unsolved maps `corridor' and `3s5z_vs_3s6z', leading to an improvement of 10% over QPLEX on average performance on the 14 maps. Moreover when the number of agents becomes large, LAN uses significantly fewer parameters than QPLEX or even QMIX. We thus show that LAN's structure forms a key improvement that helps MARL methods remain scalable.

We introduce a curriculum learning algorithm, Variational Automatic Curriculum Learning (VACL), for solving challenging goal-conditioned cooperative multi-agent reinforcement learning problems. We motivate our paradigm through a variational perspective, where the learning objective can be decomposed into two terms: task learning on the current task distribution, and curriculum update to a new task distribution. Local optimization over the second term suggests that the curriculum should gradually expand the training tasks from easy to hard. Our VACL algorithm implements this variational paradigm with two practical components, task expansion and entity progression, which produces training curricula over both the task configurations as well as the number of entities in the task. Experiment results show that VACL solves a collection of sparse-reward problems with a large number of agents. Particularly, using a single desktop machine, VACL achieves 98% coverage rate with 100 agents in the simple-spread benchmark and reproduces the ramp-use behavior originally shown in OpenAI's hide-and-seek project. Our project website is at https://sites.google.com/view/vacl-neurips-2021.

Building AI agents can be costly. Consider a question answering agent such as Jill Watson that automatically answers students' questions on the discussion forums of online classes based on their syllabi and other course materials. Training a Jill on the syllabus of a new online class can take a hundred hours or more. Machine teaching - interactive teaching of an AI agent using synthetic data sets - can reduce the training time because it combines the advantages of knowledge-based AI, machine learning using large data sets, and interactive human-in-loop training. We describe Agent Smith, an interactive machine teaching agent that reduces the time taken to train a Jill for a new online class by an order of magnitude.