Reinforcement Learning for Decision-Making in a Business Simulator

Garcı́a, Javier; Borrajo, Fernando; Fernández, Fernando

doi:10.1142/s0219622012500277

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…Authors of [22] developed Domain Approximation for RL, a method that takes advantage of planning to constrain the behavior of the agent to reasonable choices, and of RL to adapt to the environment, and increase the reliability of the decision-making process. Reference [23] applied RL to business simulators, effectively demonstrating the power of the former in a multitude of business decision making problems. They show that RL can be applied in a generalized domain where hundreds of parameters modify the domain behavior; where both cooperation and competition among different agents can coexist and where it is required to set multiple continuous decision variables for a given business decision.…”

Section: A Reinforcement Learningmentioning

confidence: 99%

Human-Augmented Prescriptive Analytics With Interactive Multi-Objective Reinforcement Learning

et al. 2021

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The rise of Artificial Intelligence (AI) enables enterprises to manage large amounts of data in order to derive predictions about future performance and to gain meaningful insights. In this context, descriptive and predictive analytics has gained a significant research attention; however, prescriptive analytics has just started to emerge as the next step towards increasing data analytics maturity and leading to optimized decision making ahead of time. Although machine learning for decision making has been identified as one of the most important applications of AI, up to now, prescriptive analytics is mainly addressed with domain-specific optimization models. On the other hand, existing literature lacks generalized prescriptive analytics models capable of being dynamically adapted according to the human preferences. Reinforcement Learning, as the third machine learning paradigm alongside supervised learning and unsupervised learning, has the potential to deal with the dynamic, uncertain and time-variant environments, the huge states space of sequential decision making processes, as well as the incomplete knowledge. In this paper, we propose a human-augmented prescriptive analytics approach using Interactive Multi-Objective Reinforcement Learning (IMORL) in order to cope with the complexity of real-life environments and the need for optimized human-machine collaborations. The decision making process is modelled in a generalized way in order to assure scalability and applicability in a wide range of problems and applications. We deployed the proposed approach in a stock market case study in order to evaluate the proactive trading decisions that will lead to the maximum return and the minimum risk that the user's experience and the available data can yield in combination.INDEX TERMS Big data, data analytics, human-augmented analytics, machine learning, prescriptive analytics, reinforcement learning.

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Section: A Reinforcement Learningmentioning

confidence: 99%

Human-Augmented Prescriptive Analytics With Interactive Multi-Objective Reinforcement Learning

et al. 2021

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“…While the use of distance metric learning techniques would certainly be desirable in order to induce a more powerful distance metric for a specific domain, such a consideration lies outside the scope of the present study. In this paper, we have focused only on Euclidean tasks in which Euclidean distance has been previously proven successful [8,19].…”

Section: Known and Unknown States In Continuous Action And State Spacesmentioning

confidence: 99%

“…With a probability of ψ h , a ← Π past (s); 8 With a probability of 1 − ψ h , a ← ϵ-greedy(Π new (s)) ; 9 Receive the next state s ′ , and reward, r k,h ;…”

Section: The Pr-srl Algorithmmentioning

confidence: 99%

Probabilistic Policy Reuse for Safe Reinforcement Learning

Garcı́a

Fernández

2018

ACM Trans. Auton. Adapt. Syst.

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This work introduces Policy Reuse for Safe Reinforcement Learning (PR-SRL), an algorithm that combines Probabilistic Policy Reuse and teacher advice for safe exploration in dangerous and continuous state and action reinforcement learning problems in which the dynamic behavior is reasonably smooth, and the space is Euclidean. The algorithm uses a continuously increasing monotonic risk function which allows for the identification of the probability to end up in failure from a given state. Such a risk function is defined in terms of how far such a state is from the state space known by the learning agent. Probabilistic Policy Reuse is used to safely balance the exploitation of actual learned knowledge, the exploration of new actions, and the request of teacher advice in considered dangerous parts of the state space. Specifically, the π -reuse exploration strategy is used. Using experiments in the helicopter hover task and a business management problem, we show that the π -reuse exploration strategy can be used to completely avoid the visit to undesirable situations, while maintaining the performance (in terms of the classical long-term accumulated reward) of the final policy achieved.

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“…Reinforcement learning (RL) has been actively considered in robotics (Kober et al, 2013) to accomplish industrial automation (Meyes et al, 2017;Stricker et al, 2018) and humanoid robot behaviors (Peters et al, 2003;Navarro-Guerrero et al, 2012) and in business management to guide decision making (Huang et al, 2011;García et al, 2012), pricing strategies (Kim et al, 2016;Krasheninnikova et al, 2019), and stock price prediction (Jae Won, 2001;Wu et al, 2020). The unique mechanism of RL tries to mimic the human learning process that acquires knowledge based on experience in a trial-and-error manner.…”

Section: Introductionmentioning

confidence: 99%

Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review

Girdler

Caldbeck

Bae

2022

Front. Syst. Neurosci.

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Creating flexible and robust brain machine interfaces (BMIs) is currently a popular topic of research that has been explored for decades in medicine, engineering, commercial, and machine-learning communities. In particular, the use of techniques using reinforcement learning (RL) has demonstrated impressive results but is under-represented in the BMI community. To shine more light on this promising relationship, this article aims to provide an exhaustive review of RL’s applications to BMIs. Our primary focus in this review is to provide a technical summary of various algorithms used in RL-based BMIs to decode neural intention, without emphasizing preprocessing techniques on the neural signals and reward modeling for RL. We first organize the literature based on the type of RL methods used for neural decoding, and then each algorithm’s learning strategy is explained along with its application in BMIs. A comparative analysis highlighting the similarities and uniqueness among neural decoders is provided. Finally, we end this review with a discussion about the current stage of RLBMIs including their limitations and promising directions for future research.

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Reinforcement Learning for Decision-Making in a Business Simulator

Cited by 11 publications

References 22 publications

Human-Augmented Prescriptive Analytics With Interactive Multi-Objective Reinforcement Learning

Human-Augmented Prescriptive Analytics With Interactive Multi-Objective Reinforcement Learning

Probabilistic Policy Reuse for Safe Reinforcement Learning

Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review

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