scholarly journals Knowledge-Guided Agent-Tactic-Aware Learning for StarCraft Micromanagement

2018 ◽  
Author(s):  
Yue Hu ◽  
Juntao Li ◽  
Xi Li ◽  
Gang Pan ◽  
Mingliang Xu
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Learning Strategy ◽  
State Of The Art ◽  
Game Playing ◽  
Challenging Problem ◽  
Two Stage ◽  
Exploration Process ◽  
Multi Agent ◽  
Agent Control

As an important and challenging problem in artificial intelligence (AI) game playing, StarCraft micromanagement involves a dynamically adversarial game playing process with complex multi-agent control within a large action space. In this paper, we propose a novel knowledge-guided agent-tactic-aware learning scheme, that is, opponent-guided tactic learning (OGTL), to cope with this micromanagement problem. In principle, the proposed scheme takes a two-stage cascaded learning strategy which is capable of not only transferring the human tactic knowledge from the human-made opponent agents to our AI agents but also improving the adversarial ability. With the power of reinforcement learning, such a knowledge-guided agent-tactic-aware scheme has the ability to guide the AI agents to achieve high winning-rate performances while accelerating the policy exploration process in a tactic-interpretable fashion. Experimental results demonstrate the effectiveness of the proposed scheme against the state-of-the-art approaches in several benchmark combat scenarios.

Multi-UAV-enabled AoI-aware WPCN: A Multi-agent Reinforcement Learning Strategy

2021 ◽  
Author(s):  
Omar Sami Oubbati ◽  
Mohammed Atiquzzaman ◽  
Abderrahmane Lakas ◽  
Abdullah Baz ◽  
Hosam Alhakami ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Strategy ◽  
Multi Agent ◽  
Multi Uav

scholarly journals Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence

2020 ◽  
Vol 34 (05) ◽  
pp. 7253-7260 ◽  
Author(s):  
Yuhang Song ◽  
Andrzej Wojcicki ◽  
Thomas Lukasiewicz ◽  
Jianyi Wang ◽  
Abi Aryan ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Research Community ◽  
Learning Agents ◽  
General Evaluation ◽  
Agent Learning ◽  
Multi Agent ◽  
Agent Intelligence ◽  
Training Schemes ◽  
Evaluation Platform

Learning agents that are not only capable of taking tests, but also innovating is becoming a 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 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 35 games of diverse logics and representations. Furthermore, multi-agent intelligence is still at the stage where many problems remain unexplored. Therefore, we provide a building toolkit for researchers to easily invent and build novel multi-agent problems from the provided game set based on a GUI-configurable social tree and five basic multi-agent reward schemes. 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. As such, the research community can perform comparisons under a stable and uniform standard. All the implementations and accompanied tutorials have been open-sourced for the community at https://sites.google.com/view/arena-unity/.

Multi-agent deep reinforcement learning strategy for distributed energy

Measurement ◽  
2021 ◽  
pp. 109955
Author(s):  
Lei Xi ◽  
Mengmeng Sun ◽  
Huan Zhou ◽  
Yanchun Xu ◽  
Junnan Wu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Strategy ◽  
Distributed Energy ◽  
Multi Agent

scholarly journals A Multi-Agent Control Architecture for a Robotic Wheelchair

2006 ◽  
Vol 3 (3) ◽  
pp. 179-189 ◽  
Author(s):  
C. Galindo ◽  
A. Cruz-Martin ◽  
J. L. Blanco ◽  
J. A. Fernńndez-Madrigal ◽  
J. Gonzalez
Keyword(s):  
Reinforcement Learning ◽  
Control Architecture ◽  
Daily Lives ◽  
Multi Agent ◽  
Human Skills ◽  
Overall Performance ◽  
Agent Control ◽  
Robotic Wheelchairs ◽  
Assistant Robots ◽  
Robotic Wheelchair

Assistant robots like robotic wheelchairs can perform an effective and valuable work in our daily lives. However, they eventually may need external help from humans in the robot environment (particularly, the driver in the case of a wheelchair) to accomplish safely and efficiently some tricky tasks for the current technology, i.e. opening a locked door, traversing a crowded area, etc. This article proposes a control architecture for assistant robots designed under a multi-agent perspective that facilitates the participation of humans into the robotic system and improves the overall performance of the robot as well as its dependability. Within our design, agents have their own intentions and beliefs, have different abilities (that include algorithmic behaviours and human skills) and also learn autonomously the most convenient method to carry out their actions through reinforcement learning. The proposed architecture is illustrated with a real assistant robot: a robotic wheelchair that provides mobility to impaired or elderly people.

scholarly journals Attention-Based Fault-Tolerant Approach for Multi-Agent Reinforcement Learning Systems

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1133
Author(s):  
Shanzhi Gu ◽  
Mingyang Geng ◽  
Long Lan
Keyword(s):  
Reinforcement Learning ◽  
Noise Intensity ◽  
Fault Tolerant ◽  
State Of The Art ◽  
Learning Systems ◽  
Noisy Environments ◽  
Time Step ◽  
Malicious Behavior ◽  
Previous State ◽  
Multi Agent

The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents. Typically, an agent receives its private observations providing a partial view of the true state of the environment. However, in realistic settings, the harsh environment might cause one or more agents to show arbitrarily faulty or malicious behavior, which may suffice to allow the current coordination mechanisms fail. In this paper, we study a practical scenario of multi-agent reinforcement learning systems considering the security issues in the presence of agents with arbitrarily faulty or malicious behavior. The previous state-of-the-art work that coped with extremely noisy environments was designed on the basis that the noise intensity in the environment was known in advance. However, when the noise intensity changes, the existing method has to adjust the configuration of the model to learn in new environments, which limits the practical applications. To overcome these difficulties, we present an Attention-based Fault-Tolerant (FT-Attn) model, which can select not only correct, but also relevant information for each agent at every time step in noisy environments. The multihead attention mechanism enables the agents to learn effective communication policies through experience concurrent with the action policies. Empirical results showed that FT-Attn beats previous state-of-the-art methods in some extremely noisy environments in both cooperative and competitive scenarios, much closer to the upper-bound performance. Furthermore, FT-Attn maintains a more general fault tolerance ability and does not rely on the prior knowledge about the noise intensity of the environment.

scholarly journals The Reinforcement Learning Competition 2014

AI Magazine ◽  
2014 ◽  
Vol 35 (3) ◽  
pp. 61-65 ◽  
Author(s):  
Christos Dimitrakakis ◽  
Guangliang Li ◽  
Nikoalos Tziortziotis
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Experiment Design ◽  
Learning Problems ◽  
Game Playing ◽  
Test Bed ◽  
Learning Agents

Reinforcement learning is one of the most general problems in artificial intelligence. It has been used to model problems in automated experiment design, control, economics, game playing, scheduling and telecommunications. The aim of the reinforcement learning competition is to encourage the development of very general learning agents for arbitrary reinforcement learning problems and to provide a test-bed for the unbiased evaluation of algorithms.

scholarly journals A2CM: a new multi-agent algorithm

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 28
Author(s):  
Gabor Paczolay ◽  
Istvan Harmati
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Multiple Agents ◽  
Pursuit Evasion ◽  
The World ◽  
Multi Agent ◽  
New Algorithms ◽  
Modified Algorithm

<p class="Abstract">Reinforcement learning is currently one of the most researched fields of artificial intelligence. New algorithms are being developed that use neural networks to compute the selected action, especially for deep reinforcement learning. One subcategory of reinforcement learning is multi-agent reinforcement learning, in which multiple agents are present in the world. As it involves the simulation of an environment, it can be applied to robotics as well. In our paper, we use our modified version of the advantage actor–critic (A2C) algorithm, which is suitable for multi-agent scenarios. We test this modified algorithm on our testbed, a cooperative–competitive pursuit–evasion environment, and later we address the problem of collision avoidance.</p>

scholarly journals StackDRL: Stacked Deep Reinforcement Learning for Fine-grained Visual Categorization

2018 ◽  
Author(s):  
Xiangteng He ◽  
Yuxin Peng ◽  
Junjie Zhao
Keyword(s):  
Reinforcement Learning ◽  
Visual Information ◽  
Experimental Validation ◽  
State Of The Art ◽  
Two Stage ◽  
Visual Categorization ◽  
Fine Grained ◽  
Reward Function ◽  
Main Challenge ◽  
Labor Consumption

Fine-grained visual categorization (FGVC) is the discrimination of similar subcategories, whose main challenge is to localize the quite subtle visual distinctions between similar subcategories. There are two pivotal problems: discovering which region is discriminative and representative, and determining how many discriminative regions are necessary to achieve the best performance. Existing methods generally solve these two problems relying on the prior knowledge or experimental validation, which extremely restricts the usability and scalability of FGVC. To address the "which" and "how many" problems adaptively and intelligently, this paper proposes a stacked deep reinforcement learning approach (StackDRL). It adopts a two-stage learning architecture, which is driven by the semantic reward function. Two-stage learning localizes the object and its parts in sequence ("which"), and determines the number of discriminative regions adaptively ("how many"), which is quite appealing in FGVC. Semantic reward function drives StackDRL to fully learn the discriminative and conceptual visual information, via jointly combining the attention-based reward and category-based reward. Furthermore, unsupervised discriminative localization avoids the heavy labor consumption of labeling, and extremely strengthens the usability and scalability of our StackDRL approach. Comparing with ten state-of-the-art methods on CUB-200-2011 dataset, our StackDRL approach achieves the best categorization accuracy.

scholarly journals KnowRU: Knowledge Reuse via Knowledge Distillation in Multi-Agent Reinforcement Learning

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1043
Author(s):  
Zijian Gao ◽  
Kele Xu ◽  
Bo Ding ◽  
Huaimin Wang
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Training Phase ◽  
Knowledge Reuse ◽  
Asymptotic Performance ◽  
Significant Progress ◽  
Historical Experience ◽  
Training Performance ◽  
Knowledge Distillation ◽  
Multi Agent

Recently, deep reinforcement learning (RL) algorithms have achieved significant progress in the multi-agent domain. However, training for increasingly complex tasks would be time-consuming and resource intensive. To alleviate this problem, efficient leveraging of historical experience is essential, which is under-explored in previous studies because most existing methods fail to achieve this goal in a continuously dynamic system owing to their complicated design. In this paper, we propose a method for knowledge reuse called “KnowRU”, which can be easily deployed in the majority of multi-agent reinforcement learning (MARL) algorithms without requiring complicated hand-coded design. We employ the knowledge distillation paradigm to transfer knowledge among agents to shorten the training phase for new tasks while improving the asymptotic performance of agents. To empirically demonstrate the robustness and effectiveness of KnowRU, we perform extensive experiments on state-of-the-art MARL algorithms in collaborative and competitive scenarios. The results show that KnowRU outperforms recently reported methods and not only successfully accelerates the training phase, but also improves the training performance, emphasizing the importance of the proposed knowledge reuse for MARL.

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