Deep Reinforcement Learning for Autonomous Driving: A Survey
With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high...
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. Despite this, the majority of research in reinforcement learning and...
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning
Many challenging tasks such as managing traffic systems, electricity grids, or supply chains involve complex decision-making processes that must balance multiple conflicting objectives and...
MOMA-AC: A preference-driven actor-critic framework for continuous multi-objective multi-agent reinforcement learning
This paper addresses a critical gap in Multi-Objective Multi-Agent Reinforcement Learning (MOMARL) by introducing the first dedicated inner-loop actor-critic framework for continuous state and...
Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021)
The recent paper `"Reward is Enough" by Silver, Singh, Precup and Sutton posits that the concept of reward maximisation is sufficient to underpin all intelligence, both natural and artificial. We...
A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments
Effective residential appliance scheduling is crucial for sustainable living. While multi-objective reinforcement learning (MORL) has proven effective in balancing user preferences in appliance...
Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning
Many decision-making problems feature multiple objectives where it is not always possible to know the preferences of a human or agent decision-maker for different objectives. However, demonstrated...
Monte Carlo Tree Search Algorithms 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 a single execution of a policy. In these settings, making decisions based on the...
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...
Go-Explore for Residential Energy Management
Reinforcement learning is commonly applied in residential energy management, particularly for optimizing energy costs. However, RL agents often face challenges when dealing with deceptive and...
Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey
The majority of multi-agent system (MAS) implementations aim to optimise agents' policies with respect to a single objective, despite the fact that many real-world problem domains are inherently...
Demonstration Guided Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) is increasingly relevant due to its resemblance to real-world scenarios requiring trade-offs between multiple objectives. Catering to diverse user...
Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management
It is often challenging for a user to articulate their preferences accurately in multi-objective decision-making problems. Demonstration-based preference inference (DemoPI) is a promising approach...
Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning: A Dynamic Weight-based Approach
Many decision-making problems feature multiple objectives. In such problems, it is not always possible to know the preferences of a decision-maker for different objectives. However, it is often...
Evolutionary Strategy Guided Reinforcement Learning via MultiBuffer Communication
Evolutionary Algorithms and Deep Reinforcement Learning have both successfully solved control problems across a variety of domains. Recently, algorithms have been proposed which combine these two...
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. We explore both what policies these sets...
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...
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games
Many real-world multi-agent interactions consider multiple distinct criteria, i.e. the payoffs are multi-objective in nature. However, the same multi-objective payoff vector may lead to different...