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Found 7 papers, 2 papers with code
Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning
A significant challenge in multi-objective reinforcement learning is obtaining a Pareto front of policies that attain optimal performance under different preferences.
Multi-Objective Reinforcement Learning
reinforcement-learning
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
Pareto Conditioned Networks
In multi-objective optimization, learning all the policies that reach Pareto-efficient solutions is an expensive process.
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.
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning
Many recent successful off-policy multi-agent reinforcement learning (MARL) algorithms for cooperative partially observable environments focus on finding factorized value functions, leading to convoluted network structures.
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.
