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Demonstration Guided Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) is increasingly relevant due to its resemblance to real-world scenarios requiring trade-offs between multiple objectives.
Multi-Objective Reinforcement Learning
reinforcement-learning
Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management
It is often challenging for a user to articulate their preferences accurately in multi-objective decision-making problems.
Go-Explore for Residential Energy Management
We use the Go-Explore algorithm to solve the cost-saving task in residential energy management problems and achieve an improvement of up to 19. 84\% compared to the well-known reinforcement learning algorithms.
Preference Inference from Demonstration in Multi-objective Multi-agent Decision Making
It is challenging to quantify numerical preferences for different objectives in a multi-objective decision-making problem.
Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning: A Dynamic Weight-based Approach
In such problems, it is not always possible to know the preferences of a decision-maker for different objectives.
