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Found 21 papers, 4 papers with code
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.
Multi-Objective Reinforcement Learning
reinforcement-learning
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
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
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.
Multi-Objective Reinforcement Learning
reinforcement-learning
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.
Go-Explore for Residential Energy Management
We use the Go-Explore algorithm to solve the cost-saving task in residential energy management problems and achieve an improvement of up to 19. 84\% compared to the well-known reinforcement learning algorithms.
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.
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.
Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning: A Dynamic Weight-based Approach
In such problems, it is not always possible to know the preferences of a decision-maker for different objectives.
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
Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models
In such settings a set of optimal policies must be computed.
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.
Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making
In this case, to apply multi-objective reinforcement learning, the expected utility of the returns must be optimised.
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.
Exploring the Impact of Tunable Agents in Sequential Social Dilemmas
In our work, we demonstrate empirically that the tunable agents framework allows easy adaption between cooperative and competitive behaviours in sequential social dilemmas without the need for retraining, allowing a single trained agent model to be adjusted to cater for a wide range of behaviours and opponent strategies.
Multi-Objective Reinforcement Learning
reinforcement-learning
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games
We contribute novel actor-critic and policy gradient formulations to allow reinforcement learning of mixed strategies in this setting, along with extensions that incorporate opponent policy reconstruction and learning with opponent learning awareness (i. e., learning while considering the impact of one's policy when anticipating the opponent's learning step).
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 dimensional environments.
A utility-based analysis of equilibria in multi-objective normal form games
In multi-objective multi-agent systems (MOMAS), agents explicitly consider the possible tradeoffs between conflicting objective functions.
Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey
We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures, and which and how utility functions are applied.
Vulnerable road user detection: state-of-the-art and open challenges
Correctly identifying vulnerable road users (VRUs), e. g. cyclists and pedestrians, remains one of the most challenging environment perception tasks for autonomous vehicles (AVs).
Exploring applications of deep reinforcement learning for real-world autonomous driving systems
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo.
