Papers Multi-Objective Reinforcement Learning
“Multi-Objective Reinforcement Learning” 태그가 달린 논문 143편 · 필터 해제
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning
Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a methodology to automa…
Decision MakingMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1gTLO: A Generalized and Non-linear Multi-Objective Deep Reinforcement Learning Approach
In real-world decision optimization, often multiple competing objectives must be taken into account. Following classical reinforcement learning, these objectives have to be combined into a single reward function. In cont…
Deep Reinforcement LearningDeep-Sea Treasure, Image versionMulti-Objective Reinforcement Learningreinforcement-learning+2Automating Staged Rollout with Reinforcement Learning
Staged rollout is a strategy of incrementally releasing software updates to portions of the user population in order to accelerate defect discovery without incurring catastrophic outcomes such as system wide outages. Som…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration
In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems of…
Bayesian OptimizationMulti-Objective Reinforcement LearningEvolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing
This paper studies the trajectory control and task offloading (TCTO) problem in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where a UAV flies along a planned trajectory to collect computation …
Edge-computingMulti-Objective Reinforcement LearningReinforcement Learning (RL)Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach
Penetration Testing plays a critical role in evaluating the security of a target network by emulating real active adversaries. Deep Reinforcement Learning (RL) is seen as a promising solution to automating the process of…
Deep Reinforcement LearningMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning
The issue of fairness in recommendation is becoming increasingly essential as Recommender Systems touch and influence more and more people in their daily lives. In fairness-aware recommendation, most of the existing algo…
FairnessMulti-Objective Reinforcement LearningRecommendation Systemsreinforcement-learning+1Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration
In constrained multi-objective RL, the goal is to learn a policy that achieves the best performance specified by a multi-objective preference function under a constraint. We focus on the offline setting where the RL agen…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
Since the inception of Recommender Systems (RS), the accuracy of the recommendations in terms of relevance has been the golden criterion for evaluating the quality of RS algorithms. However, by focusing on item relevance…
DiversityMulti-Objective Reinforcement LearningRecommendation Systemsreinforcement-learning+3A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark
In this paper, the authors investigate the Deep Sea Treasure (DST) problem as proposed by Vamplew et al. Through a number of proofs, the authors show the original DST problem to be quite basic, and not always representat…
Multi-Objective Reinforcement LearningNavigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning
Urban autonomous driving in the presence of pedestrians as vulnerable road users is still a challenging and less examined research problem. This work formulates navigation in urban environments as a multi objective reinf…
Autonomous DrivingAutonomous NavigationDeep Reinforcement LearningMulti-Objective Reinforcement Learning+3Pareto Policy Adaptation
We present a policy gradient method for Multi-Objective Reinforcement Learning under unknown, linear preferences. By enforcing Pareto stationarity, a first-order condition for Pareto optimality, we are able to design a s…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making
In many real-world scenarios, the utility of a user is derived from the single execution of a policy. In this case, to apply multi-objective reinforcement learning, the expected utility of the returns must be optimised. …
Decision MakingMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1Joint Optimization of Multi-Objective Reinforcement Learning with Policy Gradient Based Algorithm
Many engineering problems have multiple objectives, and the overall aim is to optimize a non-linear function of these objectives. In this paper, we formulate the problem of maximizing a non-linear concave function of mul…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Model-predictive control and reinforcement learning in multi-energy system case studies
Model-predictive-control (MPC) offers an optimal control technique to establish and ensure that the total operation cost of multi-energy systems remains at a minimum while fulfilling all system constraints. However, this…
BenchmarkingModel Predictive ControlMulti-Objective Reinforcement Learningreinforcement-learning+1A 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. Despite this, the majority of research in reinforcement learning and decision-theoretic planni…
Decision MakingMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1Multi-Objective Reinforcement Learning based Multi-Microgrid System Optimisation Problem
Microgrids with energy storage systems and distributed renewable energy sources play a crucial role in reducing the consumption from traditional power sources and the emission of $CO_2$. Connecting multi microgrid to a d…
energy managementManagementMulti-Objective Reinforcement Learningreinforcement-learning+2Provably Efficient Algorithms for Multi-Objective Competitive RL
We study multi-objective reinforcement learning (RL) where an agent's reward is represented as a vector. In settings where an agent competes against opponents, its performance is measured by the distance of its average r…
Multi-Objective Reinforcement LearningReinforcement Learning (RL)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. In these settings, making decisions based on the average future returns is…
Decision MakingMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1Exploring the Impact of Tunable Agents in Sequential Social Dilemmas
When developing reinforcement learning agents, the standard approach is to train an agent to converge to a fixed policy that is as close to optimal as possible for a single fixed reward function. If different agent behav…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)