Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with other agents through autonomous exploration of the environment. However, learning a task from scratch is impractical due to huge sample complexity RL algorithms. For this reason, reusing knowledge can come previous experience or indispensable scale up multiagent This survey provides unifying view literature on reuse in RL. We define taxonomy solutions for general problem, providing comprehensive...

The number of Electric Vehicle (EV) owners is expected to significantly increase in the near future, since EVs are regarded as valuable assets both for transportation and energy storage purposes. However, recharging a large fleet during peak hours may overload transformers distribution grid. Although several methods have been proposed flatten peak-hour loads recharge fairly possible available time, these typically focus either on single type tariff or making strong assumptions regarding In...

Reinforcement Learning has long been employed to solve sequential decision-making problems with minimal input data. However, the classical approach requires a large number of interactions an environment learn suitable policy. This problem is further intensified when multiple autonomous agents are simultaneously learning in same environment. The teacher-student aims at alleviating this by integrating advising procedure process, which experienced agent (human or not) can advise student guide...

Data Augmentation (DA) methods – a family of techniques designed for synthetic generation training data have shown remarkable results in various Deep Learning and Machine tasks. Despite its widespread successful adoption within the computer vision community, DA natural language processing (NLP) tasks exhibited much slower advances limited success achieving performance gains. As consequence, with exception applications back-translation to machine translation tasks, these not been as...

This paper presents a novel class of algorithms, called Heuristically-Accelerated Multiagent Reinforcement Learning (HAMRL), which allows the use heuristics to speed up well-known multiagent reinforcement learning (RL) algorithms such as Minimax-Q. Such HAMRL are characterized by heuristic function, suggests selection particular actions over others. function represents an initial action policy, can be handcrafted, extracted from previous experience in distinct domains, or learnt observation....

Pollinators play a key role in biodiversity conservation, since they provide vital services to both natural ecosystems and agriculture. In particular, bees are excellent pollinators; therefore, their mapping, classification, preservation help promote conservation. However, these tasks difficult time consuming there is lack of classification keys, sampling efforts trained taxonomists. The development tools for automating assisting the identification bee species represents an important...

The mooring systems give stability to the floating platforms against environmental conditions, stabilizing platform with lines attached seabed. are among main components that guarantee safety of staff and various operations carried out on platforms. current approaches used monitor inefficient as line tension sensors expensive install, maintain, have durability problems. This article presents development two neural network-based machine learning systems: a Multilayer Perceptron (MLP) Long...

Reinforcement learning (RL) enables an agent to learn behavior by acquiring experience through trial-and-error interactions with a dynamic environment. However, knowledge is usually built from scratch and behave may take long time. Here, we improve the performance leveraging prior knowledge; that is, learner shows proper beginning of target task, using set known, previously solved, source tasks. In this paper, argue building stochastic abstract policies generalize over past experiences...

Reinforcement learning (RL) is a widely known technique to enable autonomous learning. Even though RL methods achieved successes in increasingly large and complex problems, scaling solutions remains challenge. One way simplify (and consequently accelerate) exploit regularities domain, which allows generalization reduction of the space. While object-oriented Markov decision processes (OO-MDPs) provide such opportunities, we argue that process may be further simplified by dividing workload...

Driven by recent developments in the area of Artificial Intelligence research, a promising new technology for building intelligent agents has evolved. The is termed Deep Reinforcement Learning (DRL) and combines classic field (RL) with representational power modern approaches. DRL enables solutions difficult high dimensional tasks, such as Atari game playing, which previously proposed RL methods were inadequate. However, these solution approaches still take long time to learn how actuate...

Autonomous agents are increasingly required to solve complex tasks; hard-coding behaviors has become infeasible. Hence, must learn how tasks via interactions with the environment. In many cases, knowledge reuse will be a core technology keep training times reasonable, and for that, able autonomously consistently from multiple sources, including both their own previous internal other agents. this paper, we provide literature review of methods in Multiagent Reinforcement Learning. We define an...

While reinforcement learning (RL) has helped artificial agents solve challenging tasks, high sample complexity is still a major concern. Inter-agent teaching -- endowing with the ability to respond instructions from others been responsible for many developments towards scaling up RL. RL that can leverage learn tasks significantly faster than cannot take advantage of such instruction. That said, inter-agent paradigm presents new challenges due to, among other factors, differences between...