24 papers · ranked by Valyu relevance
Mokhaled N. A. Al-Hamadani, Mohammed A. Fadhel, Laith Alzubaidi, Harangi Balazs + 2 more
'Harangi Balazs' 'Antonio Fernández-Caballero' 'Dominique Gruyer'] Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential…
Neziha Akalin, Amy Loutfi, Anne Schmitz, Cosimo Distante
This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and…
Amit Kumar Mondal
Reinforcement learning is one of the core components in designing an artificial intelligent system emphasizing real-time response. Reinforcement learning influences the system to take actions within an arbitrary environment either having previous knowledge about the environment model or not. In this paper, we present a…
Arthur Charpentier, Romuald Élie, Carl Remlinger
Reinforcement learning algorithms describe how an agent can learn an optimal action policy in a sequential decision process, through repeated experience. In a given environment, the agent policy provides him some running and terminal rewards. As in online learning, the agent learns sequentially. As in multi-armed…
Nicolas Pröllochs, Stefan Feuerriegel
Reinforcement learning refers to a group of methods from artificial intelligence where an agent performs learning through trial and error. It differs from supervised learning, since reinforcement learning requires no explicit labels; instead, the agent interacts continuously with its environment. That is, the agent…
Aske Plaat
Deep reinforcement learning has gathered much attention recently. Impressive results were achieved in activities as diverse as autonomous driving, game playing, molecular recombination, and robotics. In all these elds, computer programs have learned to solve dicult problems. They have learned to y model helicopters and…
Ardi Tampuu, Tambet Matiisen, Dorian Kodelja, Ilya Kuzovkin + 5 more
'Kristjan Korjus' 'Juhan Aru' 'Jaan Aru' 'Raul Vicente' 'Cheng-Yi Xia'] Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by…
Anushka Deshpande
The aim of this paper is twofold. First, it seeks to uncover the algorithms that humans and other animals employ for learning in decision-making strategies within non-zero-sum games, specifically focusing on fully observable iterated prisoner’s dilemma scenarios. Second, it aims to develop a new model to explain…
MyeongSeop Kim, Jung-Su Kim, Myoung-Su Choi, Jae-Han Park + 2 more
'Gianni D’Angelo' 'Arcangelo Castiglione'] Reinforcement learning (RL) trains an agent by maximizing the sum of a discounted reward. Since the discount factor has a critical effect on the learning performance of the RL agent, it is important to choose the discount factor properly. When uncertainties are involved in the…
Eric Chalmers, Santina Duarte, Xena Al-Hejji, Daniel Devoe + 2 more
Deep Reinforcement Learning is a branch of artificial intelligence that uses artificial neural networks to model reward-based learning as it occurs in biological agents. Here we modify a Deep Reinforcement Learning approach by imposing a suppressive effect on the connections between neurons in the artificial network -…
Rui Rui, Shi
This exercise proposes a learning mechanism to model economic agent's decision-making process using an actor-critic structure in the literature of artificial intelligence. It is motivated by the psychology literature of learning through reinforcing good or bad decisions. In a model of an environment, to learn to make…
He Cai, Yaoguo Luo, Huanli Gao, Jiale Chi + 1 more
In this paper, we propose a multiphase semistatic training method for swarm confrontation using multi-agent deep reinforcement learning. In particular, we build a swarm confrontation game, the 3V3 tank fight, based on the Unity platform and train the agents by a MDRL algorithm called MA-POCA, coming with the ML-Agent…
Günther Palm, Friedhelm Schwenker
Research on artificial development, reinforcement learning, and intrinsic motivations like curiosity could profit from the recently developed framework of multi-objective reinforcement learning. The combination of these ideas may lead to more realistic artificial models for life-long learning and goal directed behavior…
Beren Millidge, Mark Walton, Rafal Bogacz
An influential theory posits that dopaminergic neurons in the mid-brain implement a model-free reinforcement learning algorithm based on temporal difference (TD) learning. A fundamental assumption of this model is that the reward function being optimized is fixed. However, for biological creatures the ‘reward function’…
Jee Hang Lee, Joel Z. Leibo, Su Jin An, Sang Wan Lee
Recent investigation on reinforcement learning (RL) has demonstrated considerable flexibility in dealing with various problems. However, such models often experience difficulty learning seemingly easy tasks for humans. To reconcile the discrepancy, our paper is focused on the computational benefits of the brain's RL.…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Markus D. Solbach, John K. Tsotsos
Reinforcement Learning is a mature technology, often suggested as a potential route towards Artificial General Intelligence, with the ambitious goal of replicating the wide range of abilities found in natural and artificial intelligence, including the complexities of human cognition. While RL had shown successes in…
D. Tuzsus, A. Brands, I. Pappas, J. Peters
A key feature of animal and human decision-making is to balance the exploration of unknown options for information gain (directed exploration) versus selecting known options for immediate reward (exploitation), which is often examined using restless bandit tasks. Recurrent neural network models (RNNs) have recently…
Emma L. Roscow, Timothy Howe, Nathan F. Lepora, Matthew W. Jones
Neural activity encoding recent experiences is replayed during sleep and rest to promote consolidation of memories. However, precisely which features of experience influence replay prioritisation to optimise adaptive behaviour remains unclear. Here, we trained adult male rats on a novel maze-based reinforcement…
Elena Zamaraeva, Christopher M. Collins, Dmytro Antypov, Vladimir V. Gusev + 6 more
Crystal Structure Prediction (CSP) is a fundamental computational problem in materials science. Basin-hopping is a prominent CSP method that combines global Monte Carlo sampling to search over candidate trial structures with local energy minimisation of these candidates. The sampling uses a stochastic policy to…
Authors not listed
Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Etinosa Osaro, Yamil Colón
The application of machine learning (ML) techniques in materials science has revolutionized the pace and scope of materials research and design. In the case of metal-organic frameworks (MOFs), a promising class of materials due to their tunable properties and versatile applications in gas adsorption and separation, ML…
Jeff Guo, Vendy Fialková, Juan Diego Arango, Christian Margreitter + 4 more
Reinforcement learning (RL) is a powerful paradigm that has gained popularity across multiple domains. However, applying RL may come at a cost of multiple interactions between the agent and the environment. This cost can be especially pronounced when the single feedback from the environment is slow or computationally…