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Found 12 papers, 2 papers with code
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
Research in multi-objective reinforcement learning (MORL) has introduced the utility-based paradigm, which makes use of both environmental rewards and a function that defines the utility derived by the user from those rewards.
Multi-Objective Reinforcement Learning
reinforcement-learning
ADT: Agent-based Dynamic Thresholding for Anomaly Detection
In this paper, we model thresholding in anomaly detection as a Markov Decision Process and propose an agent-based dynamic thresholding (ADT) framework based on a deep Q-network.
Distributional Multi-Objective Decision Making
For effective decision support in scenarios with conflicting objectives, sets of potentially optimal solutions can be presented to the decision maker.
Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning
Both algorithms outperform the state-of-the-art in multi-objective reinforcement learning for the expected utility of the returns.
Multi-Objective Reinforcement Learning
reinforcement-learning
+1
Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models
In such settings a set of optimal policies must be computed.
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning
As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a methodology to automatically learn prevention strategies in combination with complex epidemic models.
Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making
In this case, to apply multi-objective reinforcement learning, the expected utility of the returns must be optimised.
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives.
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search
In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from the single execution of a policy.
Deep Reinforcement Learning: An Overview
In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern recognition, speech recognition, computer vision, and natural language processing.
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning
Recent advances in combining deep neural network architectures with reinforcement learning techniques have shown promising potential results in solving complex control problems with high dimensional state and action spaces.
Learning to predict where to look in interactive environments using deep recurrent q-learning
Bottom-Up (BU) saliency models do not perform well in complex interactive environments where humans are actively engaged in tasks (e. g., sandwich making and playing the video games).
