7 citations · 21 across the 4 of their papers we have counts for
8 papers
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search
Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers +2
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 decision…
Exploring the Impact of Tunable Agents in Sequential Social Dilemmas
David O'Callaghan, Patrick Mannion
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 r…
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games
Roxana Rădulescu, Timothy Verstraeten, Yijie Zhang +3
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 le…
Deep Reinforcement Learning for Autonomous Driving: A Survey
B Ravi Kiran, Ibrahim Sobh, Victor Talpaert +4
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…
A utility-based analysis of equilibria in multi-objective normal form games
Roxana Rădulescu, Patrick Mannion, Yijie Zhang +2
In multi-objective multi-agent systems (MOMAS), agents explicitly consider the possible tradeoffs between conflicting objective functions. We argue that compromises between competi…
Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey
Roxana Rădulescu, Patrick Mannion, Diederik M. Roijers +1
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…