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FinRL Contests: Data‐Driven Financial Reinforcement Learning Agents for Stock and Crypto Trading
Artificial Intelligence for Engineering, Volume 1, Issue 1, Page 44-68, 29 September 2025.FinRL Contests 2023–2025 explore the application of reinforcement learning in financial tasks, which are modelled as the Markov decision process (MDP). Participants specify state, reward and action to train the FinRL agents in stable market environments, advancing the development of RL‐based trading strategies in real‐world financial markets.
Keyi Wang +7 more
wiley +1 more source
Reducing Dueling Bandits to Cardinal Bandits [PDF]
, 2014 We present algorithms for reducing the Dueling Bandits problem to the conventional (stochastic) Multi-Armed Bandits problem. The Dueling Bandits problem is an online model of learning with ordinal feedback of the form "A is preferred to B" (as opposed to
Ailon, Nir +2 more
core +1 more source
Forecasting Digital Asset Return: An Application of Machine Learning Model
International Journal of Finance &Economics, Volume 30, Issue 3, Page 3169-3186, July 2025.ABSTRACT In this study, we aim to identify the machine learning model that can overcome the limitations of traditional statistical modelling techniques in forecasting Bitcoin prices. Also, we outline the necessary conditions that make the model suitable.
Vito Ciciretti +4 more
wiley +1 more source
Energy Conversion and Economics, Volume 6, Issue 3, Page 187-195, June 2025.Abstract The collaborative energy scheduling of source‐load‐energy storage has great potential to meet the active control requirements of power‐distribution networks. In this study, a federated deep reinforcement learning framework was developed to facilitate collaborative energy scheduling and maximize the total economic benefit in a distribution ...
Yanhong Yang +4 more
wiley +1 more source
Prediction of stock prices with automated reinforced learning algorithms
Expert Systems, Volume 42, Issue 2, February 2025.Abstract Predicting stock price movements remains a major challenge in time series analysis. Despite extensive research on various machine learning techniques, few models have consistently achieved success in automated stock trading. One of the main challenges in stock price forecasting is that the optimal model changes over time due to market dynamics.
Said Yasin +2 more
wiley +1 more source
A survey on deep reinforcement learning architectures, applications and emerging trends
IET Communications, Volume 19, Issue 1, January/December 2025.Abstract From a future perspective and with the current advancements in technology, deep reinforcement learning (DRL) is set to play an important role in several areas like transportation, automation, finance, medical and in many more fields with less human interaction.
Surjeet Balhara +6 more
wiley +1 more source
Bioinspired Adaptive Resource Scheduling for QoS in Mobile Edge Deployments
IET Communications, Volume 19, Issue 1, January/December 2025.This research investigates an innovative adaptive vector autoregressive moving average with exogenous variables (VARMAx)‐based bioinspired resource scheduling model, purpose‐built for mobile edge deployments. The proposed model utilises the robust principles of flower pollination optimisation (FPO) to map tasks to virtual machines (VMs), a procedure ...
Gagandeep Kaur +2 more
wiley +1 more source
Selecting a pooling equilibrium in a signaling game with a bounded set of signals [PDF]
, 2019 In this paper, we study a general class of monotone signaling games, in which the support of the signal is limited or the cost of the signal is sufficiently low and as a result, there are multiple pooling equilibria.
Ropero García, Miguel Ángel
core
Regret Minimization in Stochastic Contextual Dueling Bandits
, 2020 Wrong result with incremental contribution, major revision ...
Saha, Aadirupa, Gopalan, Aditya
openaire +2 more sources
Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem [PDF]
, 2013 This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms.
de Rijke, Maarten +3 more
core +2 more sources