홈 › Papers

Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning

2024-02-05 · Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion, Enda Howley, Richard Dazeley, Scott Johnson, Johan Källström, Gabriel Ramos, Roxana Rădulescu, Willem Röpke, Diederik M. Roijers

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. In this paper we extend this paradigm to the context of single-objective reinforcement learning (RL), and outline multiple potential benefits including the ability to perform multi-policy learning across tasks relating to uncertain objectives, risk-aware RL, discounting, and safe RL. We also examine the algorithmic implications of adopting a utility-based approach.

📄 PDF Abstract BibTeX arXiv:2402.02665

Code (0)

등록된 구현이 없습니다.

Tasks

Multi-Objective Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization

2024-06-10 · Jesse van Remmerden, Maurice Kenter, Diederik M. Roijers, Charalampos Andriotis 외

In this paper, we introduce Multi-Objective Deep Centralized Multi-Agent Actor-Critic (MO- DCMAC), a multi-objective reinforcement learning (MORL) method for infrastructural maintenance optimization, an area traditionall…

Multi-Objective Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making

2021-06-02 · Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers, Enda Howley 외

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 Making Multi-Objective Reinforcement Learning reinforcement-learning Reinforcement Learning+1

UCB-driven Utility Function Search for Multi-objective Reinforcement Learning

2024-05-01 · Yucheng Shi, Alexandros Agapitos, David Lynch, Giorgio Cruciata 외

In Multi-objective Reinforcement Learning (MORL) agents are tasked with optimising decision-making behaviours that trade-off between multiple, possibly conflicting, objectives. MORL based on decomposition is a family of …

Decision Making MuJoCo Multi-Objective Reinforcement Learning reinforcement-learning+1

Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning

2022-11-23 · Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley 외

In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from a single execution of a policy. In these settings, making decisions based on the average future returns is n…

Multi-Objective Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search

2021-02-01 · Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley 외

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…