scholarly journals Multi-agent reinforcement learning for character control

2021 ◽  
Author(s):  
Cheng Li ◽  
Levi Fussell ◽  
Taku Komura
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Computer Games ◽  
State Of The Art ◽  
Research Topic ◽  
Future Directions ◽  
Computer Animations ◽  
Survey Papers ◽  
Simultaneous Control ◽  
Multi Agent

AbstractSimultaneous control of multiple characters has been a research topic that has been extensively pursued for applications in computer games and computer animations, for applications such as crowd simulation, controlling two characters carrying objects or fighting with one another and controlling a team of characters playing collective sports. With the advance in deep learning and reinforcement learning, there is a growing interest in applying multi-agent reinforcement learning for intelligently controlling the characters to produce realistic movements. In this paper we will survey the state-of-the-art MARL techniques that are applicable for character control. We will then survey papers that make use of MARL for multi-character control and then discuss about the possible future directions of research.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

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/.

scholarly journals MAPS: Multi-Agent reinforcement learning-based Portfolio management System.

2020 ◽  
Author(s):  
Jinho Lee ◽  
Raehyun Kim ◽  
Seok-Won Yi ◽  
Jaewoo Kang
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Portfolio Management ◽  
Management System ◽  
Investment Strategy ◽  
Sharpe Ratio ◽  
Training Procedure ◽  
Learning Methods ◽  
Multi Agent ◽  
Diversified Portfolio

Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest. Most existing deep learning methods focus on proposing an optimal model or network architecture by maximizing return. However, these models often fail to consider and adapt to the continuously changing market conditions. In this paper, we propose the Multi-Agent reinforcement learning-based Portfolio management System (MAPS). MAPS is a cooperative system in which each agent is an independent "investor" creating its own portfolio. In the training procedure, each agent is guided to act as diversely as possible while maximizing its own return with a carefully designed loss function. As a result, MAPS as a system ends up with a diversified portfolio. Experiment results with 12 years of US market data show that MAPS outperforms most of the baselines in terms of Sharpe ratio. Furthermore, our results show that adding more agents to our system would allow us to get a higher Sharpe ratio by lowering risk with a more diversified portfolio.

scholarly journals Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 5883-5891
Author(s):  
Jianwen Sun ◽  
Tianwei Zhang ◽  
Xiaofei Xie ◽  
Lei Ma ◽  
Yan Zheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Critical Point ◽  
State Of The Art ◽  
Great Success ◽  
Severe Damage ◽  
Minimal Set ◽  
Adversarial Attack ◽  
Attack Strategy ◽  
Critical Moments

Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed. However, the possibility and feasibility of such attacks against Deep Reinforcement Learning (DRL) are less explored. As DRL has achieved great success in various complex tasks, designing effective adversarial attacks is an indispensable prerequisite towards building robust DRL algorithms. In this paper, we introduce two novel adversarial attack techniques to stealthily and efficiently attack the DRL agents. These two techniques enable an adversary to inject adversarial samples in a minimal set of critical moments while causing the most severe damage to the agent. The first technique is the critical point attack: the adversary builds a model to predict the future environmental states and agent's actions, assesses the damage of each possible attack strategy, and selects the optimal one. The second technique is the antagonist attack: the adversary automatically learns a domain-agnostic model to discover the critical moments of attacking the agent in an episode. Experimental results demonstrate the effectiveness of our techniques. Specifically, to successfully attack the DRL agent, our critical point technique only requires 1 (TORCS) or 2 (Atari Pong and Breakout) steps, and the antagonist technique needs fewer than 5 steps (4 Mujoco tasks), which are significant improvements over state-of-the-art methods.

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 Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

2017 ◽  
Vol 30 (4) ◽  
pp. 449-459 ◽  
Author(s):  
Zeynettin Akkus ◽  
Alfiia Galimzianova ◽  
Assaf Hoogi ◽  
Daniel L. Rubin ◽  
Bradley J. Erickson
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Brain Mri ◽  
Mri Segmentation ◽  
Future Directions

scholarly journals Hidden Trigger Backdoor Attacks

2020 ◽  
Vol 34 (07) ◽  
pp. 11957-11965 ◽  
Author(s):  
Aniruddha Saha ◽  
Akshayvarun Subramanya ◽  
Hamed Pirsiavash
Keyword(s):  
Deep Learning ◽  
Visual Inspection ◽  
State Of The Art ◽  
Extensive Study ◽  
Research Topic ◽  
Test Time ◽  
Important Research ◽  
Important Research Topic ◽  
Deep Networks ◽  
Real World Applications

With the success of deep learning algorithms in various domains, studying adversarial attacks to secure deep models in real world applications has become an important research topic. Backdoor attacks are a form of adversarial attacks on deep networks where the attacker provides poisoned data to the victim to train the model with, and then activates the attack by showing a specific small trigger pattern at the test time. Most state-of-the-art backdoor attacks either provide mislabeled poisoning data that is possible to identify by visual inspection, reveal the trigger in the poisoned data, or use noise to hide the trigger. We propose a novel form of backdoor attack where poisoned data look natural with correct labels and also more importantly, the attacker hides the trigger in the poisoned data and keeps the trigger secret until the test time. We perform an extensive study on various image classification settings and show that our attack can fool the model by pasting the trigger at random locations on unseen images although the model performs well on clean data. We also show that our proposed attack cannot be easily defended using a state-of-the-art defense algorithm for backdoor attacks.

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.

scholarly journals Rebalancing Expanding EV Sharing Systems with Deep Reinforcement Learning

2020 ◽  
Author(s):  
Man Luo ◽  
Wenzhe Zhang ◽  
Tianyou Song ◽  
Kun Li ◽  
Hongming Zhu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Charging Time ◽  
Novel Approach ◽  
The World ◽  
Net Revenue ◽  
Multi Agent ◽  
User Demand ◽  
Policy Optimization ◽  
Over Time

Electric Vehicle (EV) sharing systems have recently experienced unprecedented growth across the world. One of the key challenges in their operation is vehicle rebalancing, i.e., repositioning the EVs across stations to better satisfy future user demand. This is particularly challenging in the shared EV context, because i) the range of EVs is limited while charging time is substantial, which constrains the rebalancing options; and ii) as a new mobility trend, most of the current EV sharing systems are still continuously expanding their station networks, i.e., the targets for rebalancing can change over time. To tackle these challenges, in this paper we model the rebalancing task as a Multi-Agent Reinforcement Learning (MARL) problem, which directly takes the range and charging properties of the EVs into account. We propose a novel approach of policy optimization with action cascading, which isolates the non-stationarity locally, and use two connected networks to solve the formulated MARL. We evaluate the proposed approach using a simulator calibrated with 1-year operation data from a real EV sharing system. Results show that our approach significantly outperforms the state-of-the-art, offering up to 14% gain in order satisfied rate and 12% increase in net revenue.

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