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A practical guide to multi-objective reinforcement learning and planning
Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …
between multiple, often conflicting, objectives. Despite this, the majority of research in …
A toolkit for reliable benchmarking and research in multi-objective reinforcement learning
Multi-objective reinforcement learning algorithms (MORL) extend standard reinforcement
learning (RL) to scenarios where agents must optimize multiple---potentially conflicting …
learning (RL) to scenarios where agents must optimize multiple---potentially conflicting …
Multi-objective multi-agent decision making: a utility-based analysis and survey
The majority of multi-agent system implementations aim to optimise agents' policies with
respect to a single objective, despite the fact that many real-world problem domains are …
respect to a single objective, despite the fact that many real-world problem domains are …
Promptable behaviors: Personalizing multi-objective rewards from human preferences
Customizing robotic behaviors to be aligned with diverse human preferences is an
underexplored challenge in the field of embodied AI. In this paper we present Promptable …
underexplored challenge in the field of embodied AI. In this paper we present Promptable …
Actor-critic multi-objective reinforcement learning for non-linear utility functions
We propose a novel multi-objective reinforcement learning algorithm that successfully learns
the optimal policy even for non-linear utility functions. Non-linear utility functions pose a …
the optimal policy even for non-linear utility functions. Non-linear utility functions pose a …
Multi-objective reinforcement learning based on decomposition: A taxonomy and framework
Multi-objective reinforcement learning (MORL) extends traditional RL by seeking policies
making different compromises among conflicting objectives. The recent surge of interest in …
making different compromises among conflicting objectives. The recent surge of interest in …
The impact of environmental stochasticity on value-based multiobjective reinforcement learning
A common approach to address multiobjective problems using reinforcement learning
methods is to extend model-free, value-based algorithms such as Q-learning to use a vector …
methods is to extend model-free, value-based algorithms such as Q-learning to use a vector …
[PDF][PDF] Distributional monte carlo tree search for risk-aware and multi-objective reinforcement learning
In many risk-aware and multi-objective reinforcement learning settings, the utility of the user
is derived from the single execution of a policy. In these settings, making decisions based on …
is derived from the single execution of a policy. In these settings, making decisions based on …
Learning pareto set for multi-objective continuous robot control
For a control problem with multiple conflicting objectives, there exists a set of Pareto-optimal
policies called the Pareto set instead of a single optimal policy. When a multi-objective …
policies called the Pareto set instead of a single optimal policy. When a multi-objective …
Robust Multiobjective Reinforcement Learning Considering Environmental Uncertainties
Numerous real-world decision or control problems involve multiple conflicting objectives
whose relative importance (preference) is required to be weighed in different scenarios …
whose relative importance (preference) is required to be weighed in different scenarios …