796 research outputs found
A Study of AI Population Dynamics with Million-agent Reinforcement Learning
We conduct an empirical study on discovering the ordered collective dynamics
obtained by a population of intelligence agents, driven by million-agent
reinforcement learning. Our intention is to put intelligent agents into a
simulated natural context and verify if the principles developed in the real
world could also be used in understanding an artificially-created intelligent
population. To achieve this, we simulate a large-scale predator-prey world,
where the laws of the world are designed by only the findings or logical
equivalence that have been discovered in nature. We endow the agents with the
intelligence based on deep reinforcement learning (DRL). In order to scale the
population size up to millions agents, a large-scale DRL training platform with
redesigned experience buffer is proposed. Our results show that the population
dynamics of AI agents, driven only by each agent's individual self-interest,
reveals an ordered pattern that is similar to the Lotka-Volterra model studied
in population biology. We further discover the emergent behaviors of collective
adaptations in studying how the agents' grouping behaviors will change with the
environmental resources. Both of the two findings could be explained by the
self-organization theory in nature.Comment: Full version of the paper presented at AAMAS 2018 (International
Conference on Autonomous Agents and Multiagent Systems
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
We study a search and tracking (S&T) problem where a team of dynamic search
agents must collaborate to track an adversarial, evasive agent. The
heterogeneous search team may only have access to a limited number of past
adversary trajectories within a large search space. This problem is challenging
for both model-based searching and reinforcement learning (RL) methods since
the adversary exhibits reactionary and deceptive evasive behaviors in a large
space leading to sparse detections for the search agents. To address this
challenge, we propose a novel Multi-Agent RL (MARL) framework that leverages
the estimated adversary location from our learnable filtering model. We show
that our MARL architecture can outperform all baselines and achieves a 46%
increase in detection rate.Comment: Accepted by IEEE International Symposium on Multi-Robot & Multi-Agent
Systems (MRS) 202
Multiagent Learning Through Indirect Encoding
Designing a system of multiple, heterogeneous agents that cooperate to achieve a common goal is a difficult task, but it is also a common real-world problem. Multiagent learning addresses this problem by training the team to cooperate through a learning algorithm. However, most traditional approaches treat multiagent learning as a combination of multiple single-agent learning problems. This perspective leads to many inefficiencies in learning such as the problem of reinvention, whereby fundamental skills and policies that all agents should possess must be rediscovered independently for each team member. For example, in soccer, all the players know how to pass and kick the ball, but a traditional algorithm has no way to share such vital information because it has no way to relate the policies of agents to each other. In this dissertation a new approach to multiagent learning that seeks to address these issues is presented. This approach, called multiagent HyperNEAT, represents teams as a pattern of policies rather than individual agents. The main idea is that an agent’s location within a canonical team layout (such as a soccer team at the start of a game) tends to dictate its role within that team, called the policy geometry. For example, as soccer positions move from goal to center they become more offensive and less defensive, a concept that is compactly represented as a pattern. iii The first major contribution of this dissertation is a new method for evolving neural network controllers called HyperNEAT, which forms the foundation of the second contribution and primary focus of this work, multiagent HyperNEAT. Multiagent learning in this dissertation is investigated in predator-prey, room-clearing, and patrol domains, providing a real-world context for the approach. Interestingly, because the teams in multiagent HyperNEAT are represented as patterns they can scale up to an infinite number of multiagent policies that can be sampled from the policy geometry as needed. Thus the third contribution is a method for teams trained with multiagent HyperNEAT to dynamically scale their size without further learning. Fourth, the capabilities to both learn and scale in multiagent HyperNEAT are compared to the traditional multiagent SARSA(λ) approach in a comprehensive study. The fifth contribution is a method for efficiently learning and encoding multiple policies for each agent on a team to facilitate learning in multi-task domains. Finally, because there is significant interest in practical applications of multiagent learning, multiagent HyperNEAT is tested in a real-world military patrolling application with actual Khepera III robots. The ultimate goal is to provide a new perspective on multiagent learning and to demonstrate the practical benefits of training heterogeneous, scalable multiagent teams through generative encoding
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
Making friends on the fly : advances in ad hoc teamwork
textGiven the continuing improvements in design and manufacturing processes in addition to improvements in artificial intelligence, robots are being deployed in an increasing variety of environments for longer periods of time. As the number of robots grows, it is expected that they will encounter and interact with other robots. Additionally, the number of companies and research laboratories producing these robots is increasing, leading to the situation where these robots may not share a common communication or coordination protocol. While standards for coordination and communication may be created, we expect that any standards will lag behind the state-of-the-art protocols and robots will need to additionally reason intelligently about their teammates with limited information. This problem motivates the area of ad hoc teamwork in which an agent may potentially cooperate with a variety of teammates in order to achieve a shared goal. We argue that agents that effectively reason about ad hoc teamwork need to exhibit three capabilities: 1) robustness to teammate variety, 2) robustness to diverse tasks, and 3) fast adaptation. This thesis focuses on addressing all three of these challenges. In particular, this thesis introduces algorithms for quickly adapting to unknown teammates that enable agents to react to new teammates without extensive observations.
The majority of existing multiagent algorithms focus on scenarios where all agents share coordination and communication protocols. While previous research on ad hoc teamwork considers some of these three challenges, this thesis introduces a new algorithm, PLASTIC, that is the first to address all three challenges in a single algorithm. PLASTIC adapts quickly to unknown teammates by reusing knowledge it learns about previous teammates and exploiting any expert knowledge available. Given this knowledge, PLASTIC selects which previous teammates are most similar to the current ones online and uses this information to adapt to their behaviors. This thesis introduces two instantiations of PLASTIC. The first is a model-based approach, PLASTIC-Model, that builds models of previous teammates' behaviors and plans online to determine the best course of action. The second uses a policy-based approach, PLASTIC-Policy, in which it learns policies for cooperating with past teammates and selects from among these policies online. Furthermore, we introduce a new transfer learning algorithm, TwoStageTransfer, that allows transferring knowledge from many past teammates while considering how similar each teammate is to the current ones.
We theoretically analyze the computational tractability of PLASTIC-Model in a number of scenarios with unknown teammates. Additionally, we empirically evaluate PLASTIC in three domains that cover a spread of possible settings. Our evaluations show that PLASTIC can learn to communicate with unknown teammates using a limited set of messages, coordinate with externally-created teammates that do not reason about ad hoc teams, and act intelligently in domains with continuous states and actions. Furthermore, these evaluations show that TwoStageTransfer outperforms existing transfer learning algorithms and enables PLASTIC to adapt even better to new teammates. We also identify three dimensions that we argue best describe ad hoc teamwork scenarios. We hypothesize that these dimensions are useful for analyzing similarities among domains and determining which can be tackled by similar algorithms in addition to identifying avenues for future research. The work presented in this thesis represents an important step towards enabling agents to adapt to unknown teammates in the real world. PLASTIC significantly broadens the robustness of robots to their teammates and allows them to quickly adapt to new teammates by reusing previously learned knowledge.Computer Science
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