DOP: Deep Optimistic Planning with Approximate Value Function Evaluation

Research on reinforcement learning has demonstrated promising results in manifold applications and domains. Still, efficiently learning effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. multi-agent systems or hyper-redundant robots). To alleviate this problem, we present DOP, a deep model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) plan effective policies. Specifically, we exploit deep neural networks to learn Q-functions that are used to attack the curse of dimensionality during a Monte-Carlo tree search. Our algorithm, in fact, constructs upper confidence bounds on the learned value function to select actions optimistically. We implement and evaluate DOP on different scenarios: (1) a cooperative navigation problem, (2) a fetching task for a 7-DOF KUKA robot, and (3) a human-robot handover with a humanoid robot (both in simulation and real). The obtained results show the effectiveness of DOP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance.Comment: to appear as an extended abstract paper in the Proc. of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018), Stockholm, Sweden, July 10-15, 2018, IFAAMAS. arXiv admin note: text overlap with arXiv:1803.0029

Learning Multi-Agent Navigation from Human Crowd Data

The task of safely steering agents amidst static and dynamic obstacles has many applications in robotics, graphics, and traffic engineering. While decentralized solutions are essential for scalability and robustness, achieving globally efficient motions for the entire system of agents is equally important. In a traditional decentralized setting, each agent relies on an underlying local planning algorithm that takes as input a preferred velocity and the current state of the agent\u27s neighborhood and then computes a new velocity for the next time-step that is collision-free and as close as possible to the preferred one. Typically, each agent promotes a goal-oriented preferred velocity, which can result in myopic behaviors as actions that are locally optimal for one agent is not necessarily optimal for the global system of agents. In this thesis, we explore a human-inspired approach for efficient multi-agent navigation that allows each agent to intelligently adapt its preferred velocity based on feedback from the environment. Using supervised learning, we investigate different egocentric representations of the local conditions that the agents face and train various deep neural network architectures on extensive collections of human trajectory datasets to learn corresponding life-like velocities. During simulation, we use the learned velocities as high-level, preferred velocities signals passed as input to the underlying local planning algorithm of the agents. We evaluate our proposed framework using two state-of-the-art local methods, the ORCA method, and the PowerLaw method. Qualitative and quantitative results on a range of scenarios show that adapting the preferred velocity results in more time- and energy-efficient navigation policies, allowing agents to reach their destinations faster as compared to agents simulated with vanilla ORCA and PowerLaw

"Guess what I'm doing": Extending legibility to sequential decision tasks

In this paper we investigate the notion of legibility in sequential decision tasks under uncertainty. Previous works that extend legibility to scenarios beyond robot motion either focus on deterministic settings or are computationally too expensive. Our proposed approach, dubbed PoL-MDP, is able to handle uncertainty while remaining computationally tractable. We establish the advantages of our approach against state-of-the-art approaches in several simulated scenarios of different complexity. We also showcase the use of our legible policies as demonstrations for an inverse reinforcement learning agent, establishing their superiority against the commonly used demonstrations based on the optimal policy. Finally, we assess the legibility of our computed policies through a user study where people are asked to infer the goal of a mobile robot following a legible policy by observing its actions

Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration

We consider the problem of cooperative exploration where multiple robots need to cooperatively explore an unknown region as fast as possible. Multi-agent reinforcement learning (MARL) has recently become a trending paradigm for solving this challenge. However, existing MARL-based methods adopt action-making steps as the metric for exploration efficiency by assuming all the agents are acting in a fully synchronous manner: i.e., every single agent produces an action simultaneously and every single action is executed instantaneously at each time step. Despite its mathematical simplicity, such a synchronous MARL formulation can be problematic for real-world robotic applications. It can be typical that different robots may take slightly different wall-clock times to accomplish an atomic action or even periodically get lost due to hardware issues. Simply waiting for every robot being ready for the next action can be particularly time-inefficient. Therefore, we propose an asynchronous MARL solution, Asynchronous Coordination Explorer (ACE), to tackle this real-world challenge. We first extend a classical MARL algorithm, multi-agent PPO (MAPPO), to the asynchronous setting and additionally apply action-delay randomization to enforce the learned policy to generalize better to varying action delays in the real world. Moreover, each navigation agent is represented as a team-size-invariant CNN-based policy, which greatly benefits real-robot deployment by handling possible robot lost and allows bandwidth-efficient intra-agent communication through low-dimensional CNN features. We first validate our approach in a grid-based scenario. Both simulation and real-robot results show that ACE reduces over 10% actual exploration time compared with classical approaches. We also apply our framework to a high-fidelity visual-based environment, Habitat, achieving 28% improvement in exploration efficiency.Comment: This paper is accepted by AAMAS 2023. The source code can be found in https://github.com/yang-xy20/async_mapp

Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation

We consider the problem of multi-agent navigation and collision avoidance when observations are limited to the local neighborhood of each agent. We propose InforMARL, a novel architecture for multi-agent reinforcement learning (MARL) which uses local information intelligently to compute paths for all the agents in a decentralized manner. Specifically, InforMARL aggregates information about the local neighborhood of agents for both the actor and the critic using a graph neural network and can be used in conjunction with any standard MARL algorithm. We show that (1) in training, InforMARL has better sample efficiency and performance than baseline approaches, despite using less information, and (2) in testing, it scales well to environments with arbitrary numbers of agents and obstacles.Comment: 11 pages, 5 figures, 2 tables, 3 pages appendix, Code: https://github.com/nsidn98/InforMAR

A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case

Article number 9330612Autonomous surfaces vehicles (ASVs) excel at monitoring and measuring aquatic nutrients due to their autonomy, mobility, and relatively low cost. When planning paths for such vehicles, the task of patrolling with multiple agents is usually addressed with heuristics approaches, such as Reinforcement Learning (RL), because of the complexity and high dimensionality of the problem. Not only do efficient paths have to be designed, but addressing disturbances in movement or the battery’s performance is mandatory. For this multiagent patrolling task, the proposed approach is based on a centralized Convolutional Deep Q-Network, designed with a final independent dense layer for every agent to deal with scalability, with the hypothesis/assumption that every agent has the same properties and capabilities. For this purpose, a tailored reward function is created which penalizes illegal actions (such as collisions) and rewards visiting idle cells (cells that remains unvisited for a long time). A comparison with various multiagent Reinforcement Learning (MARL) algorithms has been done (Independent Q-Learning, Dueling Q-Network and multiagent Double Deep Q-Learning) in a case-study scenario like the Ypacaraí lake in Asunción (Paraguay). The training results in multiagent policy leads to an average improvement of 15% compared to lawn mower trajectories and a 6% improvement over the IDQL for the case-study considered. When evaluating the training speed, the proposed approach runs three times faster than the independent algorithm.Ministerio de Ciencia, Innovación y Universidades (España) RTI2018-098964-B-I00Junta de Andalucía(España) PY18-RE000

CoMIX: A Multi-agent Reinforcement Learning Training Architecture for Efficient Decentralized Coordination and Independent Decision Making

Robust coordination skills enable agents to operate cohesively in shared environments, together towards a common goal and, ideally, individually without hindering each other's progress. To this end, this paper presents Coordinated QMIX (CoMIX), a novel training framework for decentralized agents that enables emergent coordination through flexible policies, allowing at the same time independent decision-making at individual level. CoMIX models selfish and collaborative behavior as incremental steps in each agent's decision process. This allows agents to dynamically adapt their behavior to different situations balancing independence and collaboration. Experiments using a variety of simulation environments demonstrate that CoMIX outperforms baselines on collaborative tasks. The results validate our incremental policy approach as effective technique for improving coordination in multi-agent systems

Aprendizagem de coordenação em sistemas multi-agente

The ability for an agent to coordinate with others within a system is a valuable property in multi-agent systems. Agents either cooperate as a team to accomplish a common goal, or adapt to opponents to complete different goals without being exploited. Research has shown that learning multi-agent coordination is significantly more complex than learning policies in singleagent environments, and requires a variety of techniques to deal with the properties of a system where agents learn concurrently. This thesis aims to determine how can machine learning be used to achieve coordination within a multi-agent system. It asks what techniques can be used to tackle the increased complexity of such systems and their credit assignment challenges, how to achieve coordination, and how to use communication to improve the behavior of a team. Many algorithms for competitive environments are tabular-based, preventing their use with high-dimension or continuous state-spaces, and may be biased against specific equilibrium strategies. This thesis proposes multiple deep learning extensions for competitive environments, allowing algorithms to reach equilibrium strategies in complex and partially-observable environments, relying only on local information. A tabular algorithm is also extended with a new update rule that eliminates its bias against deterministic strategies. Current state-of-the-art approaches for cooperative environments rely on deep learning to handle the environment’s complexity and benefit from a centralized learning phase. Solutions that incorporate communication between agents often prevent agents from being executed in a distributed manner. This thesis proposes a multi-agent algorithm where agents learn communication protocols to compensate for local partial-observability, and remain independently executed. A centralized learning phase can incorporate additional environment information to increase the robustness and speed with which a team converges to successful policies. The algorithm outperforms current state-of-the-art approaches in a wide variety of multi-agent environments. A permutation invariant network architecture is also proposed to increase the scalability of the algorithm to large team sizes. Further research is needed to identify how can the techniques proposed in this thesis, for cooperative and competitive environments, be used in unison for mixed environments, and whether they are adequate for general artificial intelligence.A capacidade de um agente se coordenar com outros num sistema é uma propriedade valiosa em sistemas multi-agente. Agentes cooperam como uma equipa para cumprir um objetivo comum, ou adaptam-se aos oponentes de forma a completar objetivos egoístas sem serem explorados. Investigação demonstra que aprender coordenação multi-agente é significativamente mais complexo que aprender estratégias em ambientes com um único agente, e requer uma variedade de técnicas para lidar com um ambiente onde agentes aprendem simultaneamente. Esta tese procura determinar como aprendizagem automática pode ser usada para encontrar coordenação em sistemas multi-agente. O documento questiona que técnicas podem ser usadas para enfrentar a superior complexidade destes sistemas e o seu desafio de atribuição de crédito, como aprender coordenação, e como usar comunicação para melhorar o comportamento duma equipa. Múltiplos algoritmos para ambientes competitivos são tabulares, o que impede o seu uso com espaços de estado de alta-dimensão ou contínuos, e podem ter tendências contra estratégias de equilíbrio específicas. Esta tese propõe múltiplas extensões de aprendizagem profunda para ambientes competitivos, permitindo a algoritmos atingir estratégias de equilíbrio em ambientes complexos e parcialmente-observáveis, com base em apenas informação local. Um algoritmo tabular é também extendido com um novo critério de atualização que elimina a sua tendência contra estratégias determinísticas. Atuais soluções de estado-da-arte para ambientes cooperativos têm base em aprendizagem profunda para lidar com a complexidade do ambiente, e beneficiam duma fase de aprendizagem centralizada. Soluções que incorporam comunicação entre agentes frequentemente impedem os próprios de ser executados de forma distribuída. Esta tese propõe um algoritmo multi-agente onde os agentes aprendem protocolos de comunicação para compensarem por observabilidade parcial local, e continuam a ser executados de forma distribuída. Uma fase de aprendizagem centralizada pode incorporar informação adicional sobre ambiente para aumentar a robustez e velocidade com que uma equipa converge para estratégias bem-sucedidas. O algoritmo ultrapassa abordagens estado-da-arte atuais numa grande variedade de ambientes multi-agente. Uma arquitetura de rede invariante a permutações é também proposta para aumentar a escalabilidade do algoritmo para grandes equipas. Mais pesquisa é necessária para identificar como as técnicas propostas nesta tese, para ambientes cooperativos e competitivos, podem ser usadas em conjunto para ambientes mistos, e averiguar se são adequadas a inteligência artificial geral.Apoio financeiro da FCT e do FSE no âmbito do III Quadro Comunitário de ApoioPrograma Doutoral em Informátic