Search Results for author:
Found 15 papers, 6 papers with code
Fine-grained Affective Processing Capabilities Emerging from Large Language Models
Large language models, in particular generative pre-trained transformers (GPTs), show impressive results on a wide variety of language-related tasks.
A Valid Self-Report is Never Late, Nor is it Early: On Considering the "Right" Temporal Distance for Assessing Emotional Experience
In this article, we highlight the important influence of the temporal distance between a stimulus event and the moment when its experience is reported on the provided information's validity.
A Blast From the Past: Personalizing Predictions of Video-Induced Emotions using Personal Memories as Context
A key challenge in the accurate prediction of viewers' emotional responses to video stimuli in real-world applications is accounting for person- and situation-specific variation.
Model-based Reinforcement Learning: A Survey
Two key approaches to this problem are reinforcement learning (RL) and planning.
A Unifying Framework for Reinforcement Learning and Planning
Therefore, this paper presents a unifying algorithmic framework for reinforcement learning and planning (FRAP), which identifies underlying dimensions on which MDP planning and learning algorithms have to decide.
The Second Type of Uncertainty in Monte Carlo Tree Search
Monte Carlo Tree Search (MCTS) efficiently balances exploration and exploitation in tree search based on count-derived uncertainty.
Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning
Planning and reinforcement learning are two key approaches to sequential decision making.
A Temporal Difference Reinforcement Learning Theory of Emotion: unifying emotion, cognition and adaptive behavior
Emotions are intimately tied to motivation and the adaptation of behavior, and many animal species show evidence of emotions in their behavior.
The Potential of the Return Distribution for Exploration in RL
This paper studies the potential of the return distribution for exploration in deterministic reinforcement learning (RL) environments.
A0C: Alpha Zero in Continuous Action Space
A core novelty of Alpha Zero is the interleaving of tree search and deep learning, which has proven very successful in board games like Chess, Shogi and Go.
Monte Carlo Tree Search for Asymmetric Trees
Asymmetric termination of search trees introduces a type of uncertainty for which the standard upper confidence bound (UCB) formula does not account.
Efficient exploration with Double Uncertain Value Networks
This paper studies directed exploration for reinforcement learning agents by tracking uncertainty about the value of each available action.
Emotion in Reinforcement Learning Agents and Robots: A Survey
This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents.
Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning
In this paper we study how to learn stochastic, multimodal transition dynamics in reinforcement learning (RL) tasks.
Model-based Reinforcement Learning
reinforcement-learning
+2
Optimistic Risk Perception in the Temporal Difference error Explains the Relation between Risk-taking, Gambling, Sensation-seeking and Low Fear
We found that overly optimistic risk perception (outcome optimism) results in risk taking and in persistent gambling behaviour in addition to high intensity of sensations.
