AIMC Topic: Reward

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Orientation-Preserving Rewards' Balancing in Reinforcement Learning.

IEEE transactions on neural networks and learning systems Oct 27, 2022
Auxiliary rewards are widely used in complex reinforcement learning tasks. However, previous work can hardly avoid the interference of auxiliary rewards on pursuing the main rewards, which leads to the destruction of the optimal policy. Thus, it is c...
Neural Networks, Computer Reward Reinforcement, Psychology Learning Computer Simulation
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The Influence of Robots' Fairness on Humans' Reward-Punishment Behaviors and Trust in Human-Robot Cooperative Teams.

Human factors Oct 11, 2022
OBJECTIVE: Based on social exchange theory, this study investigates the effects of robots' fairness and social status on humans' reward-punishment behaviors and trust in human-robot interactions.
Punishment Altruism Trust Humans Robotics Reward
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Socially situated artificial intelligence enables learning from human interaction.

Proceedings of the National Academy of Sciences of the United States of America Sep 19, 2022
Regardless of how much data artificial intelligence agents have available, agents will inevitably encounter previously unseen situations in real-world deployments. Reacting to novel situations by acquiring new information from other people-socially s...
Reinforcement, Psychology Social Interaction Reward Social Norms Artificial Intelligence Humans
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Flexible control as surrogate reward or dynamic reward maximization.

Cognition Sep 11, 2022
The utility of a given experience, like interacting with a particular friend or tasting a particular food, fluctuates continually according to homeostatic and hedonic principles. Consequently, to maximize reward, an individual must be able to escape ...
Artificial Intelligence Personality Humans Choice Behavior Reward
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Value-free random exploration is linked to impulsivity.

Nature communications Aug 4, 2022
Deciding whether to forgo a good choice in favour of exploring a potentially more rewarding alternative is one of the most challenging arbitrations both in human reasoning and in artificial intelligence. Humans show substantial variability in their e...
Impulsive Behavior Computer Simulation Artificial Intelligence Reward Humans
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Application of Deep Reinforcement Learning to NS-SHAFT Game Signal Control.

Sensors (Basel, Switzerland) Jul 14, 2022
Reinforcement learning (RL) with both exploration and exploit abilities is applied to games to demonstrate that it can surpass human performance. This paper mainly applies Deep Q-Network (DQN), which combines reinforcement learning and deep learning ...
Reward Neural Networks, Computer Humans Reinforcement, Psychology
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Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving.

Sensors (Basel, Switzerland) Jun 29, 2022
In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing a...
Reward Automobile Driving Algorithms Neural Networks, Computer Reinforcement, Psychology
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Sharing Rewards Undermines Coordinated Hunting.

Journal of computational biology : a journal of computational molecular cell biology Jun 24, 2022
Coordinated hunting is widely observed in animals, and sharing rewards is often considered a major incentive for its success. While current theories about the role played by sharing in coordinated hunting are based on correlational evidence, we revea...
Reward Hunting Learning Animals Algorithms Humans Artificial Intelligence
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Exploring Potential Energy Surfaces Using Reinforcement Machine Learning.

Journal of chemical information and modeling Jun 16, 2022
Reinforcement machine learning is implemented to survey a series of model potential energy surfaces and ultimately identify the global minima point. Through sophisticated reward function design, the introduction of an optimizing target, and incorpora...
Machine Learning Reinforcement, Psychology Reward
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Deep Reinforcement Learning With Modulated Hebbian Plus Q-Network Architecture.

IEEE transactions on neural networks and learning systems May 2, 2022
In this article, we consider a subclass of partially observable Markov decision process (POMDP) problems which we termed confounding POMDPs. In these types of POMDPs, temporal difference (TD)-based reinforcement learning (RL) algorithms struggle, as ...
Markov Chains Algorithms Neural Networks, Computer Reward Reinforcement, Psychology
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