AIMC Topic: Reinforcement, Psychology

Clear Filters Showing 271 to 280 of 293 articles

Replay in Deep Learning: Current Approaches and Missing Biological Elements.

Neural computation
Replay is the reactivation of one or more neural patterns that are similar to the activation patterns experienced during past waking experiences. Replay was first observed in biological neural networks during sleep, and it is now thought to play a cr...

A reinforcement learning model to inform optimal decision paths for HIV elimination.

Mathematical biosciences and engineering : MBE
The 'Ending the HIV Epidemic (EHE)' national plan aims to reduce annual HIV incidence in the United States from 38,000 in 2015 to 9300 by 2025 and 3300 by 2030. Diagnosis and treatment are two most effective interventions, and thus, identifying corre...

Computational evidence for hierarchically structured reinforcement learning in humans.

Proceedings of the National Academy of Sciences of the United States of America
Humans have the fascinating ability to achieve goals in a complex and constantly changing world, still surpassing modern machine-learning algorithms in terms of flexibility and learning speed. It is generally accepted that a crucial factor for this a...

The neurobiology of deep reinforcement learning.

Current biology : CB
In this primer, Ölveczky and Gershman review concepts and advances in deep reinforcement learning and discuss how these can inform the implementation of learning processes in biological neural networks.

Reinforcement Learning: Full Glass or Empty - Depends Who You Ask.

Current biology : CB
An extension of the prediction error theory of dopamine, imported from artificial intelligence, represents the full distribution over future rewards rather than only the average and better explains dopamine responses.

Understanding collective behaviors in reinforcement learning evolutionary games via a belief-based formalization.

Physical review. E
Collective behaviors by self-organization are ubiquitous in nature and human society and extensive efforts have been made to explore the mechanisms behind them. Artificial intelligence (AI) as a rapidly developing field is of great potential for thes...

Human-level performance in 3D multiplayer games with population-based reinforcement learning.

Science (New York, N.Y.)
Reinforcement learning (RL) has shown great success in increasingly complex single-agent environments and two-player turn-based games. However, the real world contains multiple agents, each learning and acting independently to cooperate and compete w...

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.

Science (New York, N.Y.)
The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that ha...

Action-Driven Visual Object Tracking With Deep Reinforcement Learning.

IEEE transactions on neural networks and learning systems
In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control track...