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Found 8 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
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.
